You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Final Report

Assessment of Advanced Driver
Assistance and Dynamic
Control Assistance Systems
(ADAS/DCAS)

Commissioned by the FiA
November 2025

MdynamiX AG
Junkersstraße 4
87734 Benningen
www.mdynamix.de
info@mdynamix.de

Table of Contents
1

Introduction .......................................................................................................... 5

2

Objectives ............................................................................................................. 7

3

Project Team and References ............................................................................ 9

3.1

Consortium Structure ............................................................................................. 9

3.2

Partner Profile ...................................................................................................... 10

3.3

Work Package Distribution .................................................................................. 11

4

Methods............................................................................................................... 12

4.1

Project Management and Structure (WP 1) ........................................................ 14

4.2

Literature Review (WP 2)..................................................................................... 14

4.3

European Customer Satisfaction Barometer (WP 3) .......................................... 15

4.4

Meta Study and Data Analysis (WP 4) ................................................................ 18

4.5

Insight Gathering (WP 5) ..................................................................................... 19

4.6

Policy Informing (WP 6) ....................................................................................... 19

4.7

Reporting and Dissemination (WP7) ................................................................... 20

5

Literature ............................................................................................................. 21

5.1

Regulation ............................................................................................................ 21

5.2

Systems and Safety Impact ................................................................................. 23

6

Results ................................................................................................................ 31

6.1

European Consumer Survey: ADAS Satisfaction Barometer ............................. 31

6.2

ADAS penetration study ...................................................................................... 49

6.3

European traffic safety statistics .......................................................................... 51

7

Discussion and insights ................................................................................... 52

7.1

KPI reports ........................................................................................................... 52

7.2

European report ................................................................................................... 55

7.3

Predictions on DCAS step 3 ................................................................................ 56

8

Policy recommendations .................................................................................. 57

8.1

Information and Awareness ................................................................................. 57

8.2

Information and Data ........................................................................................... 58

8.3

Improving ADAS quality ....................................................................................... 60

List of Figures ................................................................................................................ 61
List of Tables ................................................................................................................. 62
Literature ........................................................................................................................ 63

Report: Assessment of Advanced Driver Assistance and Dynamic Control Assistance Systems (ADAS/DCAS)

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Executive Summary
Study results in a nutshell
The potential to increase traffic safety can be better achieved when ADAS are developed
and introduced from a human-centered perspective. User engagement, satisfaction, acceptance and trust are key factors for the usage of ADAS.
For ACC: user acceptance, trust and usage levels are on high levels and therefore impacting road safety positively.
For LKA: the research results are less satisfying, trust and usage are below a certain
level. Safety potential is therefore not being exploited. LKA performance is especially unsatisfying on country roads and e.g. in bad weather situations. Unfortunately, those areas
of application where the greatest safety gains are expected.
Countries with better ADAS acceptance and trust show more engaged and informed
users, advanced road infrastructure and car park specificities.

The performance, user acceptance and safety impacts by driver assistance systems build the
focus of research in this study. Users are more likely to adopt and use technology when they
believe it provides clear benefits or added value. In this context e.g., safety and comfort benefits are key user expectations regarding assistance systems. This perception not only shapes
attitudes toward technology but also strengthens technology-related trust, which further facilitates acceptance. Road safety impacts by ADAS or DCAS are therefore linked to acceptance
(and related expectations) as well as to trust levels of users.
Data collection and analysis are built on the following empirical foundations:
I.
II.

a comprehensive secondary data collection and analysis,
statistical analysis of country and car park data, and,

III.

an extensive European consumer survey.

The European consumer survey is an important foundation of the study. The survey sample
covers almost 13,500 responses from a wide set of countries (with a certain influence by Germany, Austria, Denmark and Switzerland). The survey illustrates key influencing factors and
their interrelations, showing how e.g., performance in system precision, perceived security and
stress reduction impacts satisfaction, trust, and ultimately system use. By mapping these connections, the survey highlights the central fields of action that need to be addressed in order
to improve acceptance und usage of ACC and LKA.

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Figure 1: overview of user satisfaction scores for ACC and LKA across countries
Key survey results for ACC: this system is positively evaluated by users, with key strengths
identified in reliability, perceived safety benefits, and comfort. These factors lead to a high
overall satisfaction level (mean score: 4.04), which in turn translates into a high level of trust
(mean score: 3.76). This positive evaluation for ACC is reflected in actual usage behaviour:
69% of drivers report using ACC frequently or all times, while only 12.8% actively switch the
system off. Moderate complaints about system intervention are observed, yet they do not substantially undermine the positive assessment. Overall, the results indicate that ACC achieves
both high satisfaction and trust, which are closely linked to widespread and consistent system
use.
Key survey results for LKA: deficits for LKA in precision, perceived security, stress reduction,
and system intervention directly undermine user satisfaction, as expectations for safety and
comfort remain unmet. This dissatisfaction translates into even lower trust levels (mean =
2.80), with nearly one third of drivers not using the system (29.7%) and 30.7% actively switching LKA off.
Overall, the survey shows clear differences in user perception, satisfaction, trust, and acceptance for ACC and LKA. Across countries, these patterns remain relatively consistent: ACC
performs strongly overall, while LKA’s weaknesses limit trust, acceptance and finally usage.
One country is different compared to the others: Denmark. Danish drivers are more satisfied,
show higher trust and finally use ADAS more intensively. A deep dive into the Danish data
shows specificities, such as:
•

stronger technology openness and engagement with ADAS.

•

better familiarity with ACC and LKA.

•

less complaints, e.g. about system interventions.

•

lower switch-off rates, i.e. for LKA.

•

car park specificities and good road infrastructure.

•

pro-active communication concept (e.g. by FDM) about ADAS and safety impacts.

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On the other end of the spectrum Austria and Italy are more skeptical, while Switzerland and
Germany perform slightly above the average EU scores.
Literature review concludes that UNECE regulations - especially UN R171- form the core
framework for safe ADAS deployment by enforcing continuous driver monitoring and clarifying
responsibilities in Level 2 systems, complementing system-specific standards like UN R157
and UN R79. Across numerous studies, ADAS technologies - most notably AEB, LKA/LDW,
ACC, and ISA - show substantial crash-reduction potential, though their real-world effectiveness depends heavily on system design, environmental conditions, and user behavior. Remaining challenges for advancing toward DCAS phase 3 include ensuring reliable driver readiness, robust fallback mechanisms, clear operational domains, and improved user understanding to prevent misuse and over-reliance.
Statistical road and car park data: The analysis on fleet penetration performed on German
and Italian used car market data reveals new-vehicle penetration quota for systems LKA, ACC,
and Parking Assistance, exceeding quotas of 80% in Germany for LKA. EU traffic safety-relevant statistics reveal however that there is still potential to increase safety impact of these
systems.

Figure 2: New-vehicle ADAS penetration quotas in Germany
Policy recommendations: We propose to further bridge the gap between ADAS safety potential and safety effect by tackling three identified areas of concern. First, we propose a set
of measures to help ensure that drivers better understand use, limitations, and safety benefits
of ADAS. Furthermore, we promote introducing a set of measures targeted at improving data
transparency to help better understand safety impact and identify areas of improvement for
ADAS systems, infrastructure compatibility, as well as existing policies. Lastly, we propose that
with the increasing number of ADAS present in the fleet comes an increasing need to improve
both across-system reliability and ODD standards and more standards for ADAS – related user
interfaces.

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Outlook and future implications: Moderate/poor survey results for LKA systems and the
limited acceptance of a future of autonomous vehicles indicate that consumer acceptance in
DCAS phase 3 will not improve unless issues identified in the study are better addressed.

Figure 3: Expected development of ADAS acceptance metrics (trust, use, and switch-off rates)

Contacts/authors:
Prof. Bernhard Schick
bernhard.schick@mdynamix.de

Prof. Dr. Rolf Jung
rolf.jung@hs-kempten.de

Gioele Micheli
gioele.micheli@mdynamix.de

Prof. Dr. Uwe Stratmann
uwe.stratmann@hs-kempten.de

Florence Wagner
florence.wagner@hs-kempten.de

Overall project lead. Focus on human
centric driving studies and data
analysis.

User satisfaction, acceptance and trust
studies, here ADAS customer
barometer.

Functional safety, safety of the intended functionality and cybersecurity.

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1

Introduction

Driver assistance systems (ADAS) and automated driving (AD) are becoming increasingly important and will shape the future of mobility. They are key components of technological innovations in the automotive industry. People are seeking more individuality and safety, while also
wanting to make the most of their travel time. Whether for relaxation, work, communication, or
simply enjoying the driving experience, these technologies offer new possibilities.
NCAP has played a pivotal role in advancing and promoting vehicle safety standards worldwide. By providing transparent and easily understandable safety ratings, NCAP has driven
both manufacturers and consumers to prioritize safety in vehicle design and purchasing decisions. On other hand the European Union has introduced increasingly stricter regulations for
the integration of ADAS in new vehicles. The General Safety Regulation (EU) 2019/2144 was
adopted, which mandates that all new vehicles must be equipped with certain ADAS, such as
Intelligent Speed Assistance (ISA), alcohol interlock installation facilitation, driver drowsiness and attention warning, advanced driver distraction warning, emergency stop signal, reversing detection and event data recorder. This regulation is mandatory for implementation in
all EU member states starting from July 6, 2022. Since July 2024, every newly sold car in the
EU must be equipped with a Lane-Keeping Assistant (LKA) that can detect solid road markings. This requirement is regulated under UNECE R79. Overall, Euro NCAP and EU regulations are leading to safety features like ADAS becoming increasingly standard in vehicles,
aiming to raise the overall safety level on the roads and reduce the number of traffic accidents
and injuries. In contrast, Adaptive Cruise Control (ACC) is not mandatory, but it has become
widely adopted due to the significant comfort benefits it offers.
Despite early announcements in 2018 about highly automated driving systems (HAD) at Norm
SAE J3016 Level 3, the market launch was delayed due to significant technical challenges in
development, testing, validation, approval, and homologation. In 2022, Mercedes introduced
Level 3 systems in Europe, followed by BMW in 2023, while Audi plans to launch them earliest
in 2027. Level 2/2+ systems are already referred to as Driver Control Assistance Systems
(DCAS) and are regulated under UNECE R171.
The transition from level 2 ADAS systems, where the driver remains responsible, to level 3
systems (HAD) is also very significant. For this reason, there is an increasing trend in the
Automotive industry toward introducing so-called Level 2+ systems, aiming to bridge this gap
both technically and in terms of user acceptance. With Level 2+/2++ systems, the driver is
allowed to take their hands off the steering wheel, but they must keep their attention on the
traffic and remain ready to act at any time. In contrast, with Level 3 systems, the driver can
look away from the traffic and is only required to be "perception ready." This can act as a
bridging technology to facilitate the introduction of Level 3 systems, as all stakeholders can
gain experience and knowledge. However, it could also hinder the path to further automation,
as cheaper system components and fewer sensors may prevent the leap to Level 3. Additionally, there are further questions and complexities, as the Level 2+ system may offer a handsoff feature, but the vehicle may not be within its ODD (Operational Design Domain). How this
case with limited system setup can be secured is, for example, difficult to answer.

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The potential of this technology is immense, yet the complexity in its development and usage
creates barriers. Numerous driving studies and market research by MdynamiX and the IFM –
Institute for Driver Assistance and Connected Mobility at Kempten University, involving various
assistance functions in over 40 studies with more than 1,000 participants, have shown relatively low customer acceptance. While customers desire more automated functions, they often
feel overstrained [44], stressed [45][46], and do not trust the system enough. The maturity level
of some current systems is estimated to be around 50% by subjects [7]. Although market penetration is increasing, driven by NCAP and the EU-wide commitment, the adoption rate of freely
selectable ADAS/AD functions remains moderate today, when compared against the overall
vehicle fleet. [4]. The studies also reveal significant differences between gender, age, experience, occupation, and premium versus volume brands [9]. As people increasingly hand over
control to the vehicle and disengage from the driving process, user experience, sense of safety,
resulting trust, and associated technology acceptance play a central role [7] and are key to
success. If, in the future, passengers turn away from the driving experience, the comfort experience will also change significantly. Ultimately, the success of automated driving will be determined more by customer acceptance, purchase decisions, the fulfillment of user promises,
trust, and recommendations than by technology availability – technology for people [4]. Only
when people understand and trust the system, buy, use, and enjoy it while understanding its
limitations will the potential benefits for society, safety, and economic regions ultimately
emerge.
The partners MdynamiX and the Institute for Driver Assistance and Connected Mobility at
Kempten University (IFM) have built extensive expertise for the study offered here through
numerous industrial research projects and publicly funded initiatives. Unique insights have
been gained from over 40 consecutive driving studies with interviews involving more than 1,000
participants. Driving studies were also conducted in other countries, such as with Volkswagen
do Brasil [10], to examine the cultural influence and relevant market requirements. Additionally,
a customer satisfaction barometer has been developed at the IFM, with regular surveys conducted. The database contains additionally over 1,000 interviews. The close collaboration with
the automotive industry also provides crucial technological insights and roadmap initiatives.
This expertise can be applied in the offered study. Extensive publications on this can be found
in the appendix. A In addition, regular communication and proximity to FiA and the experts
from the clubs help to optimally address the needs of this study.
The following study will primarily focus on the following three cases: Intelligent Speed Assistance (ISA), Lane-Keeping Assistant (LKA), and Adaptive Cruise Control (ACC). Additionally,
efforts will be made to gain insights into Driver Control Assistance Systems (DCAS) as a Level
2 system.

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2

Objectives

The primary aim of this study is to evaluate the implementation, effectiveness, and potential
risks associated with ADAS/DCAS in real-world driving conditions. This aligns with the focus
of Institute for Driver Assistance and Connected Mobility (IFM) on developing methods and
specifications for new driver assistance systems, as well as validating their safety and reliability. Additionally, MdynamiX commitment to optimizing the driving experience through humancentered research and advanced testing & evaluation methods supports these objectives.
Additionally, the study analyzes the performance and reliability of ADAS/DCAS technologies
and assess their integration with existing road infrastructure. This objective is closely tied to
IFM’s research into connected mobility and their holistic simulation and testing methods to
ensure system performance and safety. MdynamiX expertise in driving dynamics, automated
driving, and the use of simulation environments and Hardware-in-the-Loop (HiL) solutions further supports this objective by focusing on system performance and safety.
The study also aims to provide insights into the opportunities and challenges associated with
ADAS/DCAS technologies. This is in line with IFM’s research into connected mobility, which is
crucial for understanding the broader implications and integration of these technologies. MdynamiX’s human-centered research, which focuses on user needs and human-machine interaction, helps in identifying and addressing these opportunities and challenges.
To support policymakers and stakeholders, the study develops recommendations aimed at
enhancing system performance, ensuring regulatory compliance, and building public trust in
ADAS/DCAS technologies. This aligns with IFM’s commitment to advancing driver assistance
and automated driving functions, as well as their collaboration with industrial partners to meet
regulatory and safety standards. MdynamiX’s focus on optimizing the driving experience and
user acceptance through human-centered research contributes to building public trust and ensuring regulatory compliance.
The study primarily focuses on the following three cases: Intelligent Speed Assistance (ISA),
Lane-Keeping Assistant (LKA), and Adaptive Cruise Control (ACC). Additionally, efforts are
made to gain insights into Driver Control Assistance Systems (DCAS) as a Level 2 system as
well as on DCAS phase 3.

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Emphasis Areas:
•

System Reliability and Safety Risks: Focus on identifying and mitigating safety risks,
supported by IFM’s validation of safety and reliability and MdynamiX simulation and
testing methods.

•

User Acceptance, User Experience, and Barriers to Adoption: Study user acceptance
and experience, and identify barriers to adoption, leveraging MdynamiX human-centered research.

•

Road Safety Impact: Evaluate the impact of ADAS/DCAS technologies on road safety,
aligning with IFM’s research into functional safety and connected mobility.

This structured approach ensures a comprehensive evaluation of ADAS/DCAS technologies,
addressing both technical and human factors to support their successful implementation and
adoption.

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3

Project Team and References

3.1

Consortium Structure

The consortium is composed of MdynamiX AG (Referred to as MX) and the Institute for Driving
Assistance Systems and Connected Driving (Referred to as IFM), part of the Kempten University of Applied Sciences of Kempten. MdynamiX is responsible for coordinating the project (WP
1), ensuring deadlines are met and the outcome quality is high.

Figure 4: Benningen research area

Official contact person and address for this project is:
Bernhard Schick
Junkersstraße 4 | Shelter 16
87734 Benningen
GERMANY
Phone: +49 152 56284593
E-Mail: bernhard.schick@mdynamix.de

For follow up questions regarding the project, the following person can be contacted:
Gioele Micheli
Phone: +49 151 54605240
E-Mail: gioele.micheli@mdynamix.de

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3.2

Partner Profile

MdynamiX AG
MdynamiX is exceptionally well-suited to meet the requirements of the FIA study on Advanced
Driver Assistance Systems (ADAS) and Dynamic Control Assistance Systems (DCAS). The
organization holds an ISO 9001 certification for quality management and a TISAX certification
for information security, emphasizing our commitment to high standards in research practices
and data security. With a dedicated team of 60 employees, experts in different fields, MdynamiX possesses the capacity and expertise to conduct thorough and innovative analyses.
The company's holistic approach to vehicle development ensures comprehensive evaluations
of ADAS/DCAS systems, considering all aspects of vehicle performance and safety. Our collaboration with various vehicle original equipment manufacturers (OEMs) provides practical
insights and real-world data, which are crucial for assessing system performance and integration. By bridging the gap between academic research and industry application, MdynamiX ensures that its findings are both theoretically sound and practically relevant.
Headquartered near the Benningen research area (Figure 3), MdynamiX can facilitate easier
collaboration and data collection. The organization boasts an interdisciplinary collective of experts in driving dynamics and acoustics, ensuring a well-rounded analysis of ADAS/DCAS systems. With extensive experience in research projects and publishing, MdynamiX has a proven
track record in conducting and disseminating research.
These strengths collectively enhance MdynamiX's credibility and capability to effectively contribute to the FIA study, aligning well with the study's objectives of assessing the implementation, effectiveness, and potential risks of ADAS and DCAS in real-world driving conditions.

Institute for Driver Assistance and Connected Mobility (IFM) at Kempten University of
Applied Sciences:
The IFM focuses on research and development in the areas of driver assistance systems
(ADAS) and connected mobility. The institute is directly located on the Fakt Motion testing
grounds with direct access, in a technology campus right next to Continental, ABD, rfpro, MdynamiX, Expleo, and many others. The institute works on various cutting-edge technologies in
automotive engineering, particularly those that enhance vehicle safety, automation, and the
integration of new mobility solutions. IFM is known for conducting comprehensive studies and
projects, often in collaboration with industry partners, to advance knowledge in areas like autonomous driving, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication,
as well as user experience and acceptance of new technologies. It also engages in both industrial and publicly funded research projects, contributing valuable insights into the development and implementation of innovative automotive systems. The institute supports not only
academic studies but also provides practical, real-world insights for the automotive industry.
Its work is often focused on improving safety, optimizing mobility systems, and exploring the
technological and societal impacts of emerging automotive technologies. With its close ties to

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the automotive sector, the institute is positioned as a key player in advancing the future of
mobility and driving technologies. The philosophy of the IFM is based on the idea of bringing
people and future technologies together, conducting user-centered research in the context of
automated driving and vehicle dynamics. A team of more than 50 employees works closely
with the automotive industry and has access to state-of-the-art laboratory, testing, and simulation facilities. Notably, the new driving simulator is worth mentioning, as it features a unique
6D motion system with exceptionally high dynamics and agility.

3.3

Work Package Distribution

Table 1: Overview of consortium members and tasks assigned (MX: MdynamiX, IFM: Institute
for Driving Assistance and Connected Mobility
Member (Affiliation)

Role and Expertise

Prof. Bernhard Schick (MX, IFM)

Expert for ADAS/AD technologies, evaluation,
human centric driving studies and data analysis

Prof. Dr. Uwe Stratmann (IFM)

Expert for market and consumer research in the
field of the international automotive industry.
Responsible for the European customer barometer in this study (see i.e. chapter 6.1)

Prof. Dr. Rolf Jung (IFM)

Expert for functional safety, safety of the intended functionality and cybersecurity

Florence Wagner (IFM)

Functional safety, safety of the intended functionality and cybersecurity research and studies

Seda Aydogdu (MX)

UX- User experience and human centric driving
studies and survey

Gioele Micheli (MX)

Human factor and User Experience, human
centric driving studies and data analysis

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4

Methods

The project follows a work package approach. Each work package is a compact collection of
tasks associated with a distinct methodical approach. Each work package has a work package
lead that is responsible for ensuring the work packages results are of expected quality and
delivered in time. There are two supportive work packages (Project Management and Reporting and Dissemination), three information-gathering work packages (Literature review, Consumer Survey, and Data analysis), and two information-summarizing work packages (Insight
gathering and Policy Informing).

Index

Title

WP1

Project Management

WP2

Literature Review

WP3

Stakeholder Interviews

WP4

Meta Study and Data Analysis

WP5

Insight Gathering

WP6

Policy Informing

WP7

Reporting and Dissemination

Figure 5: Work package structure

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Figure 6: Focus countries participating and their respective clubs
The analysis is centered on so-called Focus countries in Europe. These countries are selected
to serve as account for socio-economic, cultural, or regional differences. The project is centered on a limited number of countries to gather more in-depth information on these regions.
Country selection is based on factors such as affiliation with European sub-region, availability
of FiA partners for survey-distribution, and availability of public ADAS-related data.
This work follows a KPI-based approach to data collection. This means that from objectives
established, a set of comprehensive KPIs are formulated that are the basis for all research
conducted. In this way, all results are streamlined to answer the question of how well the KPI’s
targets are met. To establish well-defined target values, extensive research projects would
have to be conducted that would go beyond this project’s scope. Instead, fulfillment of criteria
is drawn on the basis of facts and data (drawn from the information-gathering work packages)
upon which the respective work package lead form a verdict in the manner of an expert-rating.
The resulting verdict is then categorized into three labels:
•

Below average / not fulfilled (Dashboard color red): KPIs is significantly below average
values of other systems or countries or does not fulfill policy-driven goals.

•

Average performance / fulfillment (Dashboard color yellow): KPIs are performing averagely good and fulfill or are on track of fulfilling policy-driven goals.

•

Good performance / exceeding fulfillment (Dashboard color green): KPIs are fulfilled
beyond average or exceed policy-driven goals.

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4.1

Project Management and Structure (WP 1)

Project management is a supportive work package that ensures success in all other work
packages. For this, a project lead is selected among the consortium to lead all efforts. The
project manager is responsible for ensuring deadlines are met while work package output quality is high, aligning project efforts with objectives. The project manager will lead consortiuminternal dialogue and resolve difficulties to guarantee objectives are met. The project manager
is the primary contact point for FiA and organizes regular meetings. The work package’s outcome is the successful completion of all other work packages.
Subordinate tasks:
•

Ensure deadlines are met in time

•

Ensure high-quality output for all work packages

•

Track alignment with objectives

•

Lead intra-project communication

•

Lead contractor’s side in meetings with FiA

•

Result: High quality, timely results in all work packages

4.2

Literature Review (WP 2)

Research is conducted to create an overview of ADAS/DCAS technological SotA and outlook,
research on the SotA of the regulatory landscape in the EU and finally research on safety and
reliability of this system with the focus on the cases Intelligent Speed Assistance (ISA), LaneKeeping Assistant (LKA), and Adaptive Cruise Control (ACC).
The method of a systematic literature review (SLR) is applied to this work package. The structured, transparent and replicable process to identify, evaluate and synthesize existing research
on this topic is the standard approach. The method begins by defining clear research questions
and inclusion/exclusion criteria. A comprehensive research strategy is developed to locate relevant studies across multiple databases. Retrieved articles are screened for relevance and
data is extracted. The quality of the studies included is assessed using established appraisal
tools. Finally, the findings are synthesized for the meta-analysis and summarized to provide a
comprehensive overview of current knowledge, SotA and gaps.
This systematic approach focuses on
Research Questions:
ADAS/DCAS reliability, safety, and risks
User acceptance and user experience of ADAS/DCAS
Barriers to adoption of the systems
ADAS/DCAS safety impact

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Exclusion and inclusion Criteria:
Where are the topic boundaries
What is the focus
Which systems are of interest (ISA/LKA/ACC)
What literature must be included (Regulations)
Selection of Literature:
Screening process and literature gathering
Data extraction
Documentation of results

4.3

European Customer Satisfaction Barometer (WP 3)

4.3.1 Analysis Model
Theoretical foundation
Customer satisfaction and inherent trust play a pivotal role in the development of automated
driver assistance systems (ADAS), not only for ensuring market success but also for advancing
road safety. Systems such as adaptive cruise control (ACC) and lane keeping assistance (LKA)
- already widely deployed - significantly impact the perception of safety among drivers. This
perception influences trust and acceptance, both crucial for the broader adoption of these technologies and, eventually, fully autonomous driving.
By understanding the interplay between customer expectations, satisfaction, and behavior,
important stakeholders such as the group of vehicle manufacturers can refine systems to address not just convenience but also critical safety concerns. The insights from customer satisfaction studies are instrumental in designing systems that drivers perceive as both reliable and
lifesaving, thus fostering safer roads and supporting the evolution toward autonomous mobility.
Customer satisfaction, trust, and acceptance of new technologies are closely linked in a reinforcing cycle. High customer satisfaction builds trust, which increases the likelihood of users
accepting and adopting new technology. In turn, when technology meets user expectations
and is easy to use, it boosts satisfaction and deepens trust. Together, these factors drive continued usage and loyalty. Different studies provide empirical evidence for the strong relationship between trust, acceptance and usage of new technologies (e.g. Lee and See, 2004; Endsley, 2017; Kraus et al., 2020).
Usage rates of ADAS systems are therefore linked to customer expectations, their fulfillment
and the resulting trust. This is the linking pin between analyzing customer satisfaction and
advancing road safety. Even if the penetration of ADAS systems shows an impressive development the final trust and usage is deciding about the impact on safety development.
Beyond that, future success of ADAS innovations and autonomous driving system largely depends on technology acceptance: how willing and able users are to adopt and integrate new

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technologies into their routines. If users perceive the innovation as useful and easy to use,
they are more likely to adopt it (e.g. Davis, 1985). High acceptance leads to widespread use,
which is critical for an innovation to gain traction, deliver value, and succeed in the market.
Without user acceptance, even the most advanced innovation can fail. That is the fundamental
hypothesis of the Technology-Acceptance Model by Davis (1985). And various empirical evidence underlines the importance of that theory.
Applied analysis model for the European satisfaction barometer
Customer satisfaction is the result of a subjective comparison of expectations and experiences
with a particular product or service. According to the confirmation/disconfirmation paradigm
(which goes back to Anderson, 1973), the identification and characterization of customer expectations is a key factor for effective customer satisfaction management. In the case of newto-the-world innovations, these expectations are usually not known or only known to a limited
extent, which in turn makes it difficult to develop new products that meet the needs of the target
customer group.
Customer satisfaction is substantial in terms of acceptance of and trust in an innovation. According to Technology-Acceptance Model (Davis, 1985) these are important prerequisites for
the subsequent market success of new products. ADAS such as lane keeping assistance or
adaptive cruise control have been installed for several years and now have high market penetration.
Anderson’s confirmation/disconfirmation theory (Anderson, 1973) provides the fundamental
theoretical concept for analyzing customer satisfaction. For the present research the theory is
supplemented by behavioral aspects. This comparison process is subjective and individual to
the customer, since cognitive and affective factors influence the resulting satisfaction.

Figure 7: Analysis concept of the customer satisfaction study

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4.3.2 Methodology of the customer survey and sample profile
A quantitative customer survey is selected as the main data collection technique. In addition,
qualitative expert interviews were conducted to discuss and justify certain hypotheses and
survey findings.
Survey methodology
The research is in particular analyzing following ADAS systems: adaptive cruise control (ACC)
and lane keeping assistance (LKA). Latest systems like the lane change assistant were not
covered in this survey due to low penetration of these systems in the car parc.
Car drivers were asked about following core aspects:
•

General relevance of ADAS out of customer perspectives

•

Core expectations towards ADAS in general and specificly for ACC and LKA

•

Fulfillment of customer expectations and the resulting

•

Customer satisfaction levels, which lead to a certain level of

•

Trust against ACC and LKA

•

Usage profiles and rates

•

Drivers of satisfaction and dissatisfaction

•

Future outlook: acceptance of autonomous driving

An online questionnaire was designed according to these ADAS aspects. The questionnaire
was translated into the local country language and distributed via online channels for the different countries in question. The distribution was done by the local clubs (like ADAC etc.).
Sample selection and profile
The dataset comprises 13,374 respondents, with Germany (6,362), Austria (4,813) and Denmark (1,164) forming the largest subgroups and together accounting for 92.3% of the total
sample. Switzerland (484), Luxembourg (119), Italy (114), and other markets (318) are represented by smaller case numbers. However, given the relatively low variance of results across
countries, even these smaller samples provide meaningful insights for cross-country comparisons.
Table 2: Sample profile description

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Across all groups, the median vehicle registration year is 2021, reflecting a modern fleet. It is
striking that e.g., the vehicle age has no significant impact on driver satisfaction (will be discussed later). Driving styles are predominantly moderately sporty, without a distinct defensive
or offensive tendency. Reported usage covers a balanced mix of city, country, and highway
driving, with many respondents covering more than 15,000 km annually.
Overall, the sample represents frequent drivers with strong technical affinity and familiarity with
advanced driver assistance systems (ADAS). In summary, the dominant user profile in samples is improving the quality of the survey outcome. An overview of the sample profile is provided by table 2 (see below).

4.4

Meta Study and Data Analysis (WP 4)

4.4.1 ADAS penetration study

Figure 8: method of ADAS penetration study
To improve transparency in available ADAS penetration numbers, an empirical approach to
forming an understanding of past and current development is chosen. For this, platforms of
used car markets in chosen focus countries are chosen. The method’s goal is to determine the
ratio of vehicles equipped with a certain ADAS in comparison with all new vehicles, both by
year and by country.
To find this value, for each reseller market, a set of quality criteria is then used to reduce false
reports in results. Quality criteria can include requirements like professional merchant-only resale offers. The frequency and thereby the ADAS rates for each system are computed. Methodically, numbers computed in this fashion are susceptible to false positive reporting and
false negative reporting. By computing false positive and false negative rates for each year, a
statistical model can be used to adjust the computed rates.

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Based on the new vehicle penetration rate, the fleet penetration by year can then be computed
to allow for insights into the relation between system safety and outcomes.

4.5

Insight Gathering (WP 5)

Drawing on the results of WP 2-5, a thorough analysis is made to further answer the overarching research questions in a complete approach. For this, we summarize all individual work
package results and integrate them in a dashboard analysis. The summary will be structured
hierarchically from the central objective: Creating an evidence-based foundation on the basis
of which recommendations for policy makers and other stakeholders can be given. The hierarchical approach subordinates the key objectives, and KPI are subordinate to the specified
objectives. At the base level, all results cater to KPI-based dashboards showcasing values for
current state, potential for improvement, and challenges found, combining both quantitative
and qualitative findings.
Subordinate tasks:
•

Work package coordination

•

Summarize all insights

•

Create full, evidence-based picture of current ADAS landscape

•

Expert workshops

•

Evaluate fulfillment of all objectives and answer related questions

•

Create solid basis for policy informing

•

Result: KPI – based analysis and dashboards

4.6

Policy Informing (WP 6)

The work package involves coordinating efforts to deduce recommendations from evidencebased research, tailored to various stakeholders. In order to help the stakeholders to
enhance system performance, ensure regulatory compliance and build public trust in
ADAS/DCAS, policy informing is conducted. Methodically, the evidence-based research and
data analysis is used as a foundation. Based on results the policy recommendations are developed and derived during a structured discussion workshop. To represent stakeholders’ interests and views the participation of experts from academia and research with experience in
automotive industries and development as well as regulation and decision making is important
to the discussion. Stakeholder-dependent, actionable recommendations are elaborated as a
result of this work package.

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4.7

Reporting and Dissemination (WP7)

The aim of this work package is documenting continuously all results, preparing presentations
for regular exchange with FiA, writing reports and being responsible for the content of public
presentations.
Coordinating regular meetings with colleagues, experts and initiators of this project to maintain
a constant exchange of the status of results, new ideas and findings is the procedure to track
the progress and timeline of the project. Dashboards for presentations and content for reports
are created subsequent to regular meetings. All findings and results from research, reviews,
analysis, studies and discussions are documented and visualized in a final report and presentation.

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5

Literature

5.1

Regulation

Regulatory Requirements for ADAS Systems Based on UNECE Standards: A Focus on UN
R157, R171, and Related Regulations.
UN Regulation No. 171 (DCAS) plays a critical role in regulating driver assistance systems,
particularly those operating at SAE Level 2, where both the system and the driver are involved
in control tasks. It complements other UNECE regulations, such as UN R79 (Steering Functions) and UN R157 (ALKS), by focusing on driver engagement, system validation, and clear
user communication. While other regulations address the technical aspects of individual systems (lane keeping, lane change), UN R171 ensures driver monitoring and safety in combined
systems (e.g., ACC + LKA). It mandates continuous driver availability and interaction with the
system, offering a framework for safe integration of increasingly automated vehicles into realworld environments. This regulation fills a crucial gap in current automotive standards, enhancing safety and functionality in Level 2 systems.
The regulatory framework under UNECE standards aims to harmonize the safety and functionality of Advanced Driver Assistance Systems (ADAS), ensuring their safe operation. UN
R157 focuses on Automated Lane Keeping Systems (ALKS), UN R171 addresses Driver Control Assistance Systems (DCAS), and UN R79 covers Steering Assistance for systems like
Lane Keeping Assist (LKA). These regulations work in tandem to support the development of
Level 2 and Level 3 automated driving functions. However, UN R171 is particularly significant
in bridging the gap between lower-level ADAS and full automation by enforcing strict driver
monitoring and system engagement requirements, ensuring that vehicles operating with ACC,
ISA, and LKA features maintain a high standard of safety while the driver remains actively
responsible.

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Table 3: Key UNECE Regulations for ADAS Systems

Regulation

Scope / Function

Key Requirements

Driver Responsibility
/ Monitoring

UN R157 "Automated

Lane-keeping, longitu-

− System only acti-

Driver must be ready

Lane Keeping Systems (ALKS)"

dinal control on specific roadways (e.g.,
motorways)

vated on roads with
physical separation.

to take over if needed;
remains responsible.

− Minimum sensing
range of 46m, full lane
width coverage. − Minimum risk maneuver
for fallback in case of
driver non-intervention.

UN R171 "Driver
Control Assistance
Systems (DCAS)"

Level 2 systems with
sustained longitudinal
and lateral support
(e.g., ACC + LKA)

− Continuous driver
monitoring (hands on
wheel, gaze/head) and
disengagement warn-

Driver remains in control; must remain engaged with the system
at all times.

ings. − Clear user
communication on system limitations.
− Validation of system’s functional safety
and operational domain.

UN R79 "Steering
Equipment / Lane

Assesses systems
providing steering as-

− Tests for lane-keeping and lane-changing

Driver remains responsible; system supports,

Keeping Assist"

sistance (LKA)

functionality.

but does not take over
control.

− Steering system
safety: forces, failure
behavior.

The introduction of UN R171 is essential to the overall UNECE framework as it specifically
addresses the growing complexity of ADAS operating at Level 2, which combines multiple
systems such as ACC and LKA to provide sustained control assistance while maintaining the
driver's responsibility. Unlike UN R79 and UN R157, which primarily focus on individual systems, UN R171 emphasizes driver monitoring and continuous engagement, ensuring that the
driver is both aware of the system's limitations and able to intervene when necessary. This
regulation is crucial for bridging the gap between semi-automated systems and fully autonomous vehicles. Moreover, it helps manufacturers comply with functional safety and system
validation standards, ensuring that ADAS technologies are both effective and safe for realworld use. [38-42]

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5.2

Systems and Safety Impact

State-of-the-art ADAS systems now feature advanced multi-sensor fusion, safety-critical functions like ACC, LDW/LKA, BSD, ISA, and continue evolving toward personalized, cooperative,
and AR-enhanced capabilities. Human factors remain key—driver trust, training, and understanding are ongoing challenges. Regulation, through DCAS, is formalizing safety benchmarks
and paving the way for higher-level ADS deployment. As these systems advance, achieving
the balance between automation, driver engagement, and robust certification remains essential.
Modern ADAS deploy a range of sensor technologies—radars, cameras, LiDAR, ultrasonics—
to deliver functions like Adaptive Cruise Control (ACC), Blind‑Spot Detection (BSD), Lane Departure Warning (LDW)/Lane Keeping Assist (LKA), Intelligent Speed Assistance (ISA), and
Emergency Brake Assist (AEB).
Adaptive Cruise Control (ACC) uses mainly radar, lidar, camera and sensors to maintain safe
spacing from preceding vehicles and can include “Stop & Go” and predictive (GPS-informed)
features. Lane assistance systems warn of unintended lane departure (LDW), gently steer to
keep the vehicle within its lane (LKA or Lane Centering Assist), or take over in emergencies
(Automated Lane Keeping Systems – ALKS). Blind‑spot detection helps drivers avoid lanechange collisions through visual, audible, or haptic alerts. ISA systems prevent speeding by
warning or actively reducing speed based on road‑limit data, offering active or passive behavior.
Evaluations show that ADAS enhances safety and comfort, though challenges like false
alarms, insufficient precision, and inconsistent user interfaces remain.
Through the literature considered the safety aspect of ADAS systems is summarized as positive. Studies, reports and conclusions represent the experiences from middle European countries and the USA. A range of safety impact and accident reduction numbers are given based
on different data pool, analysis methods and inclusion/exclusion criteria. For future ADAS applications the given statements are considered as safety potential which cannot be proven by
now.
Masello et al. [28] states that a full ADAS deployment could lead to a 29% decrease in accident
frequency, resulting in an estimation of 18,925 fewer crashes in the UK. According to this
study, the AEB has the most significant impact on road safety since it is effectively reducing
accidents in most frequent accident types. LDW and ACC also contribute to the effective accident reduction, but their impact is generally less compared to AEB. 23% accident reduction
potential is mentioned for LDW. For ACC the effectiveness varies based on driving context and
primarily aids in reducing rear-end and collision-related accidents.
Results from an exploratory analysis of ADAS features and their safety outcomes in the USA
indicate that improved safety outcomes are associated with the presence of three ADAS features: lane departure warning, forward collision warning, and blind spot detection [37].

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Vehicles with ADAS features were less likely to be involved in fatal and severe crashes like
head-on and rear-end crashes. Another statement published in this paper declares that more
robust data is urgently needed for disentangling the safety effects of ADAS.
Swedish analysis of the LDW/LKA system in [26] shows a positive effect in reducing lane departure crashes. LDW/LKA systems were estimated to lower the driver injury risk in crash types
that the systems are designed to prevent (head-on and single-vehicle crashes).
The meta-analysis study from Wang et al. [31] considers 73 studies across six different countries and technologies. Most interesting findings conducted that AEB has the greatest safety
impact among the evaluated technologies and is significantly contributing to crash reduction.
It is estimated to reduce around 1369099 crashes per year across the six countries studied.
Accounting for an approximate 19.34% avoidance to total crashes. Both ACC and LKA contribute to improving safety, particularly in scenarios involving lane maintenance and longitudinal control, but their individual impact is less than that of technologies like AEB. ACC is estimated to reduce approximately 102,447 crashes per year, accounting for about 1.45% of total
crashes. LKA results in a reduction of about 128,290 crashes annually, representing approximately 1.81% of total crashes.
Safety advantages and improvements from ADAS influencing other road participants is discussed in “Effect of Advanced Driver Assistance Systems (ADAS) on Pedestrian Safety” [30]
and mentions ACC Effect of Advanced Driver Assistance Systems (ADAS) on Pedestrian
Safety. LKA is covered under LDW systems, which help prevent unintentional lane drifts that
could endanger pedestrians by ensuring the vehicle stays within its lane. The study indicates
that ADAS features, such as Automatic Emergency Braking (AEB) and Pedestrian Detection,
significantly improve pedestrian safety by reducing both the frequency and severity of accidents. Real-world data analyses demonstrate that vehicles equipped with ADAS experience
fewer pedestrian accidents, primarily because these systems can quickly recognize potential
collisions and initiate braking faster than human drivers are capable of.
Moreover, simulation studies and effectiveness assessments reveal that ADAS technologies
effectively prevent or mitigate pedestrian collisions under various conditions, including low visibility and complex urban environments. However, some challenges remain, such as false positives/negatives and environmental sensitivity, which can affect overall effectiveness.
Within the study to evaluate the safety impact of ACC in [29] the following results are summarized: The study evaluates the impact of ACC parameter settings on rear-end collisions on
freeways, particularly in traffic oscillations. Results indicate that safety impacts are largely affected by ACC parameters, with smaller time delays and larger time gaps improving safety,
and the combination of ACC and variable speed limits (VSL) achieving better safety improvements in congested freeways, especially with ACC penetration rates less than 30%. ACC systems can reduce collision risks in congested traffic if properly designed with appropriate parameter settings, such as larger time gaps, smaller time delays, and greater maximum deceleration rates.

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Accident reduction and reduction of collision severity is subject to the “Towards Zero Accidents
Analysis of Advanced Technologies Enabling Safe Roads” paper. [27] Content shows that
ADAS technologies such as AEB, Forward Collision Warning, and Lane Departure Warning
Systems actively prevent crashes: AEB systems can detect imminent collisions and apply
brakes automatically, significantly reducing rear-end accidents. Lane assistance systems help
prevent accidents caused by drowsiness, distraction, or poor visibility. The impact is leading
to fewer crashes, especially those involving inattentive or fatigued drivers and reduced severity
when accidents occur. Also, this article indicates that ADAS has the potential to encourage
safer diving habits over time, influencing long-term behavioral change and safer driving culture.
Concluding, the ADAS technologies do not eliminate risk, but they mitigate it significantly by
reducing reliance on human reflexes, enhancing situational awareness, and automatically intervening when needed.
Safety potential of ADAS is measured by the Austrians [11] and resulting in the following: This
study analyzes the road safety impact of nine ADAS using Austrian crash data and considering
factors like infrastructure, weather, market penetration, and user acceptance. Results indicate
that warning/braking ADAS have the greatest future reduction potential with Intelligent Speed
Assistance also contributing significantly. Crash reduction potentials were calculated for Adaptive Cruise Control (ACC), Adaptive Lighting, Alcohol-Interlock system, warning/breaking
ADAS (AEB, FCW), Intelligent Speed Assistance (ISA), Curve-ABS, Lane Keeping/Departure
Assistance (LKA/LDA), Turning Assistant, and ADAS regarding drowsiness. All ADAS support
a reduction in crashes, fatalities, and injuries, even when considering risks like inattentive driving and limited functionality. The greatest potential is for warning/braking ADAS (AEB, FCW),
potentially reducing approximately 8,700 crashes and 70 fatalities in Austria by 2040, a 24%
reduction compared to the 2016-2020 average. Intelligent Speed Assistant (ISA) is the second
most promising, potentially reducing overall crashes by 8% in 2040. The results for ACC indicate that it has a limited but meaningful impact on crash reduction. According to the study,
ACC, along with other ADAS such as lane assistance and adaptive lighting, shows potential
to reduce crashes by around 8% in 2040 compared to current figures. Specifically, ACC is
expected to contribute to the overall crash and casualty reduction, although its impact is
smaller relative to warning/braking systems like AEB/FCW, which exhibit the highest potential.
Additionally, ACC plays a role in preventing crashes related to driver drowsiness and concentration lapses, especially when combined with other ADAS measures. LKA has significant potential for crashes and fatality reduction. The study estimates that LKA could reduce the number of fatalities by up to 90–100 persons in 2030 and 2040. It is identified as one of the ADAS
with the highest potential in terms of fatalities prevented, especially when considering severe
injuries and fatalities associated with lane departure crashes. In order to exhaust the full potential of ADAS safety advantages the user behavior is significant.
Tan et al. [12] present a summary of evidence for the crash avoidance effectiveness of ADAS
in their paper. In this study, three common methods for safety benefit evaluation were identified: Field operation test (FOT), safety impact methodology (SIM), and statistical analysis
methodology (SAM).

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Table 4: Overview on ADAS-safety related studies

Results from this study are pictured in the table and highlight that the ACC technology is
aligned with the rear-end crash type and has an avoidance effectiveness of 13% [19]. Another
study indicates an avoidance effectiveness of 12% [23] and a third with 14% [20]. A combination of ACC and FCW is set for a rear-end crash type and giving an effectiveness of 16% [22].
ACC and AEB in combination result in a high score with 45% crash avoidance effectiveness.
[24] LKA is considering the lane-departure crash type and presenting different results with 35%
[25], 20% [21] and 30% [24]. For the LKA head-on, single crashes type the avoidance effectiveness is 32%. [26]
In a retrospective cohort study in the USA [14] analysis estimates the effectiveness of ADAS
systems helping to prevent system-relevant crashes. Numbers resulting from the analysis indicate that AEB-equipped vehicles were 43% less likely (Hazard Ratio = 0.57) to be the striking
vehicle in a front-to-rear crash compared to non-equipped vehicles. The analysis was also
stratified to look at the effect in intersection versus non-intersection crashes. BSM-equipped
vehicles were 4% less likely (Hazard Ratio = 0.96) to be involved in a same-direction sideswipe, though the differences were not significant. LKA-equipped vehicles were 9% less likely
(Hazard Ratio = 0.91) to run off the road. LDW and LKA did not have a significant effect on
risk of same-direction sideswipe or head-on crash.
German studies for accident prevention and ADAS systems performed by the Bundesanstalt
für Straßen- und Verkehrswesen (BASt) [18] presents that ACC can significantly reduce rear-

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end collisions, especially in situations such as sudden braking maneuvers or other vehicles
suddenly rear-ending them. Particularly in heavy traffic on highways, ACC significantly reduces
the vehicle's reaction time and brakes early, which reduces the likelihood of a collision. The
study confirms that ACC systems reduce the overall number of accidents, primarily through
early and situation-dependent braking in critical distance situations, which significantly reduces
both the frequency and severity of rear-end collisions. The most important factors influencing
effectiveness are correct parameterization, system reliability, and the situations in which ACC
is activated. Regarding LKA the studies and simulation results show that LKA reduces the
likelihood of unintentional lane departure, which can reduce accidents caused by side or headon collisions. LKA can be particularly effective in supporting drivers who are tired or distracted.
A reduction potential of 50% considering runway accidents is mentioned while these results
and the effectiveness of the system are dependent on sensor data, detection limits, and response times. Along with the benefits from ADAS technologies on road safety the challenges
are highlighted in this report: At the same time, emphasis is placed on requirements for technology, infrastructure, and acceptance in order to achieve maximum social benefit. Efficiency
and safety must be supported by system development, standardization, and legal frameworks.
Recent articles have started a discussion about ACC not contributing to road safety but in fact
increasing the crash rates. [15] Otherwise, this article supports safety improvements by means
of AEB and LKA. It is important to note that the ACC’s negative effect is based on only one
study published by Netherlands insurance and mostly indicates and reveals that the data an
analysis on real-world impact of ADAS is scarce.
Reliability of ADAS and their performance is often dependent on factors like road types,
weather, lighting, road infrastructure, traffic density for example. According to users of ACC
systems the vehicle can behave unpredictably with sudden accelerations or decelerations
when a car in front changes lanes or cuts in. [34] Resulting from the given study the respondents concluded that ACC being "blind" in curves, disengaging too quickly, reacting to irrelevant
obstacles, and conservative headway settings leading to sluggish overtaking behavior. Additionally, beeping sounds when the lead vehicle disappears from radar view and dependence
on the driving skill of the car ahead were noted as displeasing aspects. Some drivers also
expressed concerns about the ACC's limitations in specific conditions like heavy rain or when
it's not functioning properly at low or very high speeds.
[16], [17] and [13] conclude that while ADAS technologies significantly contribute to road
safety, their reliability and consistency are contingent upon proper system design, regular
maintenance, and user awareness. Ensuring optimal performance requires addressing environmental challenges, maintaining sensor calibration, and educating drivers about the limitations of these systems.
The “Driver perceptions of advanced driver assistance systems and safety“ [36] explores how
drivers perceive and interact with ADAS. Systems considered and of interest are ACC and
LKA. Results are that 70% of drivers use ADAS while 40% feel that ADAS compromises their

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safety when active (mostly referring to LKA) indicating a high usage but low confidence in
ADAS. 30% of drivers report little or no knowledge of the ADAS in their vehicles. Most drivers
learn by trial and error, not through formal training. Only 7.7% took a driving course, and only
a third received any dealership training, usually under 10 minutes.
A big part of participants has no awareness of their feature since 89% know they have cruise
control, but awareness drops significantly for ACC (28.5%), LKA (25%), and parking assist
(20.3%). Additionally, there is confusion with feature names which affects the users’ understanding. Based on the study’s results the following actions are proposed:
- Improved training and education
- Transparent communication from manufacturers
- More research on real-world ADAS impacts
- Re-examination of the assumption that ADAS inherently improves safety.
According to [33] user knowledge and trust in the system are critical factors influencing
ADAS acceptance. It is highlighted that the acceptance of ADAS depends on the sources
and quality of knowledge available to users. Also, user acceptance and trust significantly influence their willingness to purchase ADAS technologies. Looking at the ADAS acceptance
across different user groups, the acceptance is mainly influenced by prior knowledge, trust,
perceived usefulness, and demographic factors in general drivers.
DeGuzman and Donmez [32] provide a survey study with the primary objective of assessing
knowledge of and trust in ACC and LKA among owners and non-owners and investigating the
relationship between knowledge and trust. The results and conclusions drawn from this study
are as follows:
•

Owning a vehicle with ACC or LKA does not appear to result in a better understanding of system limitations.

•

For both owners and non-owners, participants tended to overestimate ADAS more
than underestimate it.

•

Prior to system use (i.e., for non-owners, who had no experience with ACC or LKA),
knowledge of specific capabilities and response bias affects trust, which in turn,
affects reliance intention.

•

Once drivers have experience with the system (i.e., owners in our sample),
knowledge of specific system capabilities and response bias do not have a significant influence on trust.

•

For ACC owners, using the system more frequently is related to lower trust, which
in turn was associated with a lower reported likelihood to engage in secondary
tasks.

•

Using LKA more frequently was not associated with lower trust, potentially due to
the fact that participants were more aware of some of the common limitations, which
reduced the negative impact of system failures on trust.

According to [11] user behavior significantly influences the effectiveness of ADAS. The study
identifies several ways in which driver’s actions and attitudes can impact system performance:

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•

Incorrect use and understanding: For ADAS to be beneficial, users must operate
them correctly and be aware of their limitations. Lack of knowledge can lead to
improper handling, reducing safety gains.

•

Over-reliance and complacency: Drivers may develop a false sense of security,
leading to decreased attention and increased distraction, which can undermine system benefits and potentially cause unsafe situations.

•

Distraction and workload: The presence of ADAS can reduce driver workload, but
this might also increase inattentiveness or cause drivers to neglect actively monitoring the driving environment, especially if they trust the systems too much.

•

Behavioral adaptation (Risk Homeostasis): With ADAS, drivers may subconsciously adopt riskier behaviors, compensating for perceived safety improvements,
thus possibly diminishing the systems’ overall impact.

•

Need for driver education: To mitigate these issues, improved driver training and

clear information about ADAS capabilities and limitations are necessary, aiming to
ensure systems are used properly and effectively.
Overall, user behavior plays a crucial role in realizing the safety potential of ADAS, emphasizing the importance of education, proper system design, and awareness of system limitations.
Testing and evaluating the safety of ADAS is in scope of Euro NCAP Assessments and gives
a specific protocol on how to proceed. The Euro NCAP Assisted Driving Protocol v2.2 (2024)
evaluates the performance of modern driver assistance systems across three core testing domains: speed assistance, distance assistance, and lane keeping assistance. These tests aim
to assess the functionality, reliability, and driver interaction of such systems under realistic
driving conditions. In the speed assistance domain, systems that combine camera-based
speed limit recognition with map data achieve the highest accuracy in identifying and adapting
to speed limits, while purely vision-based systems remain prone to errors under poor visibility
or changing lighting. The distance assistance tests, typically assessing adaptive cruise control
(ACC), focus on time gaps, response to cut-ins, and reaction to sudden deceleration. Systems
using sensor fusion—integrating radar, camera, and sometimes LiDAR—demonstrate more
stable and predictable behavior, particularly in complex traffic scenarios. Lane keeping assistance evaluates a vehicle’s ability to maintain its lane through curves and under degraded road
markings; early and smooth steering interventions score best, whereas abrupt or delayed corrections are penalized. Overall, Euro NCAP’s findings highlight that the effectiveness of assisted driving depends not only on the individual system components but also on their seamless integration. A balanced and coordinated interaction among speed, distance, and lanekeeping functions is essential for achieving safe, comfortable, and trustworthy vehicle automation. [43]
Risks are maintained while a human driver is responsible and overseeing the ADAS operation.
Discussions suggest that comfort systems like ACC are more delicate for trust, over-reliance
and driving task distraction. Also, it is assumed and necessary that a responsive and responsible driver is present to operate a DCAS step 2 vehicle.

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ACC and LKA are example ADAS implemented in vehicles. While there are no real-word studies on road safety effects with DCAS step 3 implementation, only assumptions based on experiences can be drawn. Meanwhile the literature [1-10] summarizes the most significant challenges present for a DCAS step 3 implementation:
•

Driver Monitoring & Readiness: Studies focusing on how Level 3 systems monitor
driver engagement and how the driver can safely transition control back to the system.

•

Legal & Liability Challenges: Addressing legal responsibility for accidents or malfunctions, and the transition of responsibility from the vehicle to the driver.

•

System Reliability & Fallback Mechanisms: Technical concerns regarding how well
Level 3 systems handle unexpected road scenarios and potential failures, and the
need for fail-safe mechanisms.

•

Ethics & Public Trust: Ethical concerns related to driver disengagement, particularly
in critical scenarios, and how these are handled in systems with partial automation.

•

Operational Domains: Challenges around defining the geofencing and operational
boundaries where Level 3 systems can operate safely without requiring full driver
control.

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6

Results

6.1

European Consumer Survey: ADAS Satisfaction Barometer

Chapter 6.1 presents the key findings of the customer satisfaction analysis. The results are
based on 13,374 responses from six core European countries. The research validity and quality of results are therefore extremely high, especially for the countries Austria, Germany and
Denmark. Switzerland, Italy and Luxembourg are considered as well even if the sample size
is lower. However, consistency checks for these countries show high validity as well – the
variance factor is low.

Total

Austria

Denmark

Germany

Italy

Luxembourg

Switzerland

Other

13,374

4,813

1,164

6,362

114

119

484

318

Further European countries with a limited sample size (n<100) are analyzed in the group of
“other countries”. The core country pattern is consistent: national differences mainly concern
the intensity of certain outcomes, not the overall direction of results. The evaluation follows the
approach of the selected analysis model, i.e., the results are presented from customer expectations to satisfaction results down to the assessments of future autonomous mobility.

6.1.1 Relevance of and engagement with ADAS systems from customers’
perspectives
Across all markets, engagement with the topic of driver assistance systems prior to vehicle
purchase is moderate to low, with a total mean score of 3.15 (see figure 9). Therefore, ADAS
does not play a very important role for users if they plan to buy a new car. ADAS systems are
not vital for brand and vehicle choice as well – less than 40% stated that ADAS is decisive for
brand and vehicle choice. The results are consistent across all countries, even if some differences can be observed.
While Germany and Switzerland show slightly higher involvement, Austria, Hungary, and Italy
remain below the overall average, just one third of users postulates intense interest in that
topic. Despite these variations, the overall differences between countries are relatively small,
indicating a broadly consistent level of pre-purchase consideration across markets.
Core research message: overall engagement with driver assistance systems prior to purchase is moderate-low. Although Germany and Switzerland reported slightly higher values, the
relatively small cross-country differences indicate a broadly consistent pattern across markets.

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Figure 9: New car customer engagement with ADAS in the new car purchasing process
(score 4: intense engagement / score 5: very intense engagement)
The next question is about familiarization with ADAS technologies by users. The results show
that self-familiarization without instructions is the most frequently reported approach across all
markets, with particularly high shares in Germany (59.3%), and especially Denmark (67.2%).
It is striking that “No conscious familiarization” is especially high in Austria (30.6%) – this could
be one reason for user acceptance problems. Dealer-based instruction reaches its highest
level in Switzerland (49.4%), Austria (47.8%) and Denmark (40.5%). But is notably low in e.g.
Germany (31.3%), Italy (31.9%) and Luxembourg (27.4%). See table 5 for a summary.

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Table 5: How users get familiarized with ADAS systems

Core research message: Self-familiarization dominates across markets (total value 57.9%),
with Switzerland showing the highest focus on dealer instruction (49%), while Austria reports
the highest shares of “no conscious familiarization” (30.6%). Greater engagement with ADAS
technologies in Denmark (in Denmark “no conscious familiarization” shows the lowest share
at all) may also lead to better customer satisfaction and acceptance.
Although the introduction to the systems reveals weaknesses, most customers feel relatively
comfortable with the systems. This is somehow a contradiction and could result in a lack of
system understanding due to the misjudgment of one's own abilities.

6.1.2 Customer expectations and fulfilment rates for ACC and LKA
Expectations are a decisive factor in the evaluation of user satisfaction with driver assistance
systems (ADAS). Satisfaction is shaped less by the absolute technical performance of a function than by the extent to which it meets or exceeds what drivers anticipate. High expectations
that remain unfulfilled often lead to disproportionately critical assessments, whereas moderate
expectations that are met can generate comparatively positive satisfaction ratings.
Consequently, a systematic consideration of user expectations is indispensable for interpreting
satisfaction scores and for identifying potential mismatches between technological performance and driver needs.
For both -ACC and LKA- the core expectations are very clear and in this order:
1. Safety plays the most important role, followed by
2. Comfort expectations, next is about
3. Stress reduction (interdependent with safety and comfort, but not mentioned as a primary factor)
4. Increased driving pleasure (which is not so relevant for users).

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For Adaptive Cruise Control (ACC), expectations are consistently high and largely met
across all dimensions, with a slight over-fulfilment in driving pleasure (see figure 10). In contrast, Lane Keeping Assist (LKA) shows markedly lower expectations and even lower fulfilment values, particularly for comfort and stress reduction.
Overall, ACC is perceived as a reliable and beneficial system, whereas LKA remains associated with limited user confidence and comparatively low satisfaction. Both systems reveal a
clear expectation–fulfilment gap, yet the magnitude differs between ACC and LKA.

Figure 10: Driver expectations and fulfillment levels for ACC
Core research message: ACC shows high expectations largely met, while LKA reveals lower
expectations and consistently weaker fulfilment. Safety and comfort benefits are in the expectation focus of users. These expectations need to be met to get satisfied users. Driving pleasure is obviously not so important to customers, so a certain degree of indifference can be
assumed here.

6.1.3 Customer satisfaction and trust for ACC and LKA
Satisfaction and trust are key drivers of user acceptance of advanced driver assistance systems (ADAS). Without both, technologies will neither be fully accepted nor consistently used
in everyday driving. Figure 11 shows the user satisfaction scores for the given countries. The
research results are very clear: across all markets, satisfaction with ACC consistently exceeds
LKA, indicating systematically higher user acceptance of ACC.
Across all markets, satisfaction with Adaptive Cruise Control (ACC) clearly exceeds that of
Lane Keeping Assist (LKA). Particularly in Austria and Luxembourg, LKA satisfaction reaches
the lowest levels (3.13 and 3.01), while Switzerland and Germany show comparatively higher
evaluations for both systems. Denmark stands out relatively clearly with good satisfaction ratings – scores are much above the average and above all countries. This is a remarkable result
for Denmark

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Figure 11: User satisfaction scores for ACC and LKA across countries
Core research message: the consistently observed gap between ACC and LKA across countries indicates a systematic difference in user acceptance, with ACC being more positively
perceived. Denmark shows significantly better results. Austria shows low levels for ACC and
LKA.
Net satisfaction score (NSS) - the conducted customer satisfaction measurement uses the
concept of the net promoter score (NPS) (Reichheld et al., 2021). For this research a net satisfaction score (NSS) is applied. The NSS identifies the share of satisfied customers versus
the dissatisfied one. For this purpose, scale ranges 1 and 2 are defined as detractors (range
of dissatisfaction). Scale ranges 3 and 4 do not indicate complete satisfaction, i.e., no enthusiasm is triggered in the customer (see also Kano, 1984). Only the scale value 5, "completely
satisfied", is considered as promoter, since only this leads to a clear positive confirmation.
Subtracting the detractors from the promoters results in the net satisfaction score (NSS). This
procedure clearly represents which state of satisfaction predominates. Theoretically, a result
spectrum between +100% (all users are completely satisfied, scale 5) and -100% (all users
are completely or partially dissatisfied, scales 1 and 2) is possible. A reading example for Austria: according to the survey results, the difference between very satisfied and not and not very
satisfied drivers is 14.1 percent points for LKA (see figure 12).
NSS results reveal a consistent divergence between Adaptive Cruise Control (ACC) and Lane
Keeping Assist (LKA). ACC achieves clearly positive net satisfaction across all countries, with
the highest values clearly observed in Denmark (57.6%) – again a strong difference to the
other countries. Denmark is the only country with a positive NSS for LKA.
In contrast, LKA records uniformly negative NSS results, with the lowest levels in Austria, Germany and Luxembourg. This systematic gap highlights ACC as a technology that is broadly
accepted and valued by users, whereas LKA faces persistent skepticism and lower satisfaction.

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Figure 12: Net satisfaction scores (NSS) for ACC and LKA across countries
Furthermore, no statistical correlation is observed between vehicle age and satisfaction with
either system. A reason for the missing correlation could be rising expectations of new car
customers – new technologies need to perform better according to rising expectations.
Trust correlates very strongly with satisfaction: low satisfaction leads to an even greater loss
of trust (see figure 13). Across all markets, satisfaction with Lane Keeping Assist (LKA) is
consistently higher than trust, with an overall mean score of 3.23 for satisfaction compared to
a mean score of 2.80 for trust.
This reflects a systematic gap of 0.43 points between satisfaction and trust, which corresponds
to a relative reduction of approximately 13% from satisfaction to trust. While the magnitude
varies slightly between countries, the decline is evident in every case, highlighting that positive
satisfaction ratings do not directly translate into equivalent trust in the system. In the LKA context – low satisfaction level reinforces low trust. Again, satisfaction and trust scores are highest
in Denmark. In comparison, Austrian drivers trust LKA the least.

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Figure 13: Mean scores for user satisfaction and user trust for LKA
According to recommendation rates, ACC is showing much better results compared to LKA.
ACC with rates consistently above 80% across countries, for total sample 86%. LKA with rates
of +/- 60% (this appears to be a poor result).
What is the gender impact on satisfaction levels for ACC and LKA? Males report higher LKA
satisfaction (3.26 vs. 3.08) and trust (2.83 vs. 2.56) than females. However, the “total” values
confirm the same pattern: moderate satisfaction, distinctly lower trust for LKA. ACC satisfaction
exceeds 4.0 for males and 3.82 for females. ACC trust values remain high for both genders,
but again on different levels (3.30 vs. 3.81).

Figure 14: LKA and ACC satisfaction and trust results by gender

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Gender impacts in the country comparison: Denmark shows the highest share of male respondents, and this demographic structure aligns with the pattern of elevated satisfaction
scores. The strong male representation coincides with the already high national satisfaction
levels, suggesting that the Danish sample combines a user group with generally positive system evaluations and a gender distribution that - based on the broader dataset - tends to report
slightly higher satisfaction. The survey results therefore indicate a certain gender impact on
ADAS acceptance.
Core research message: for LKA a mismatch between expectations and experiences is resulting in moderate/poor satisfaction scores. This satisfaction perception is influencing trust
badly what could logically impact usage behavior. For all countries trust scores are always
below 3.0 – except for Denmark. Male drivers tend to be slightly more satisfied and trust more.
Besides the correlation between gender and satisfaction, the study observes the impact by
user familiarity. The correlation seems to be very strong (see figure 15). Users who feel very
familiar with the system are e.g. satisfied with LKA (score 3.49), while that score is 1.80 for
users who say they are not familiar with LKA. The satisfaction level is twice as high – a remarkable survey result and important for later conclusions on industry implications.

Figure 15: User familiarity with technology correlates strongly with satisfaction scores
Another question is whether the road situation influences satisfaction levels – the answer
is yes. While LKA especially works well on motorways, performance on country roads seems
to be much lower. Empirical evidence is shown by figure 16: average NSS for LKA is -24% for
country roads. The corresponding score for highways is -7.5%, which is significantly better.
Improvements in traffic safety through ADAS would primarily affect rural roads, but the systems
appear to perform less well here than on highways. That seems to be a core weakness of e.g.
LKA.

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Figure 16: Impact on satisfaction by road situation*

*DK and LU not in sample

Figure 17 summarizes the first survey results: the consumer survey indicates correlation between gender, driver profiles, familiarization and road situation on one side, and satisfaction
with ADAS on the other.

Figure 17: Summary of satisfaction results

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6.1.4 Satisfaction and Dissatisfaction drivers for ACC and LKA
Overall satisfaction with advanced driver assistance systems (ADAS) results from the interplay
of multiple subfactors, such as usability, reliability, and perceived safety. To fully understand
user acceptance of systems like ACC and LKA, these dimensions need to be evaluated individually as well as in their combined effect on overall satisfaction. Following the core results
for ACC and LKA are listed.
ACC satisfaction and dissatisfaction drivers
Major ACC factors are rated very positive (table 5). The overall ACC result is therefore satisfying. ACC is rated highly for usability, safety benefits, and reliability. These core features consistently dominate user satisfaction, with particularly high values in Denmark, Switzerland and
Germany, underlining the perceived ease of use, safety benefits, and dependable performance
of the system. By contrast, features such as customizability and functioning in all road or
weather situations receive slightly lower ratings, though this pattern is consistent across most
markets.
Table 6: Drivers of user satisfaction for ACC

Some highlights of ACC evaluation by country:
•

Denmark shows consistently outstanding satisfaction across all ACC dimensions, with
top scores in operation/usability (4.34), sense of security (4.25), reliability (4.25) and
system precision (4.29). This pattern indicates a substantially higher perceived performance and trust level compared to the overall average.

•

Switzerland combines high ratings in usability (4.16), precision (3.98) and reliability
(3.97). Its profile reflects a uniformly strong system evaluation without extreme variance
across features.

•

Germany records above-average scores in key performance-related attributes such as
usability (4.04) and reliability (3.90), pointing to robust perceived system stability and
operational clarity.

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•

Luxembourg stands out selectively through strong evaluations in system precision
(4.06) and reliability (3.98), suggesting specific strengths in system accuracy, despite
more moderate values in other dimensions.

•

Austria and Italy show comparatively lower satisfaction across multiple attributes, marking them as consistently below-average countries relative to others.

LKA satisfaction and dissatisfaction drivers
LKA shows a different picture (table 6). Many factors indicate problems out of users’ perspectives. For Lane Keeping Assist (LKA), operation/usability is the only very positively rated factor,
with strongest results in Denmark, Switzerland and Germany. All other dimensions receive just
moderate or even negative evaluations, particularly customizability and functions in all road
and weather situations, which represent the weakest aspects across markets. Putting this analysis in the context of figure 16 (see chapter 6.1.3), it confirms the very weak results for LKA in
terms of “function in all road situations”. As underlined before, influence of the road situation
on the system’s performance seems to be very strong. Drivers who mostly use highways show
significant higher satisfaction compared to those who mostly use country roads.
Overall, the results highlight that while LKA is perceived as easy to use, it lacks acceptance in
terms such as reliability, adaptability, and safety: critical factors for trust and broader adoption.

Table 7: Drivers of user satisfaction for LKA

Some highlights of LKA evaluation by country:
•

Again, it is Denmark which exhibits the strongest overall evaluation of LKA, with leading
scores in usability (3.92), reaction speed (3.65), reliability (3.52) and precision (3.48).
The pattern indicates comparatively higher confidence in system responsiveness and
stability.

•

Switzerland shows consistently above-average ratings across central dimensions such
as usability (3.79), reaction speed (3.41) and action traceability (3.11). Its profile reflects
a stable mid-to-high satisfaction cluster without pronounced weaknesses.

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•

Germany performs solidly in usability (3.68) and shows moderate but balanced evaluations in other core attributes (e.g., reliability 3.13; precision 3.07), suggesting a relatively homogeneous perception without extreme deviations.

•

Austria and Italy stand out through slightly lower ratings across multiple dimensions,
marking it as comparatively critical markets in the LKA context.

Core research message: compared to Lane Keeping Assist (LKA), Adaptive Cruise Control
(ACC) shows consistently higher evaluations across all subfactors, with usability, reliability,
and perceived security as clear strengths. LKA, by contrast, is rated positively only for usability,
while all other dimensions remain negative, highlighting fundamental limitations in user trust
and acceptance.

6.1.5 Usage profiles for ACC and LKA
After analyzing the satisfaction and trust levels, the next question is about usage rates. The
results show clear differences in the use and deactivation of ACC and LKA.
ACC achieves the highest positive values for active use, indicating that a majority of drivers
employ the system frequently and only rarely switch it off. In contrast, LKA exhibits lower active
use scores and substantially higher switch-off rates, reflecting limited user acceptance and
more frequent disengagement.

Figure 18: Usage and switch off rates for ACC and LKA (total sample)

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The red segments of the chart highlight negative extremes, i.e., respondents who either do not
use the system at all or who very often switch it off. For LKA, the red share is clearly larger in
both dimensions, indicating a substantial proportion of drivers who actively avoid or disable the
system.
By contrast, ACC shows much smaller red segments, reflecting that only a minority refrains
from use or frequently deactivates it. This pattern underscores a markedly higher application
intensity and everyday integration of ACC compared to LKA.
However, it is remarkable that Danish drivers use ACC and LKA significantly more often. The
correlation seems to be clear: drivers in Denmark are more satisfied, have higher trust and use
ACC and LKA much more often. This is a remarkable study outcome and underlines how vital
satisfaction and trust for technology acceptance and trust is.
Core research message: the data highlight ACC as a system integrated into everyday driving,
while LKA is more often actively deactivated and thus less trusted in practice. The relationship
between satisfaction, trust and usage seems to be very strong.
Top 3 reasons for deactivation of LKA (total sample):
The survey concept incorporated main reasons why users de-active LKA. Core reasons are:
1. Unpleasant intervention of the system: 54.4%
2. Lack of reliability: 32.8%
3. Unpleasant system warnings: 27.9%
Unpleasant intervention of the system is especially an issue for German users (62.5%) while
again users from Denmark complain less (30.9%) But even for Denmark it is the most important
reason to switch LKA off.
Top 3 reasons for deactivation of ACC (total sample):
Even if the share of people who actively de-actives ACC is very low, what are the reasons for
deactivation? The core results are:
1. Unpleasant intervention of the system: 41.3%
2. Loss of control: 22.5%
3. Lack of reliability: 18.4%
Again, deactivation is driven by unpleasant intervention of the system. Again, German users
are complaining the most about that issue (50.9%). Danish drivers complain less (just 16.7%)
– their main reason for deactivation of ACC is loss of control (29.4% - which is above the
average).

6.1.6 Consequences for system improvements
Satisfaction and trust results require a deeper dive into those determinants which could improve performance of ACC and LKA. Following, core aspects for ADAS improvement are described. Therefore, users were asked to name the core aspects to improve ACC and LKA.

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Top 5 aspects to improve performance of LKA
Across all markets, the strongest improvement needs for Lane Keeping Assist (LKA) concern:
1.
2.
3.
4.

system precision (45.2%),
reliability (35.3%),
function across different road situations (34.3%),
traceability of system actions (29.7%), and

5. personalization (26.8%).
Aspects such as function in all-weather situations (25.1%), operation (14.4%), reaction speed
(11.9%), and sense of security (13.3%) are mentioned less frequently, indicating comparatively
lower priority compared to the points mentioned before. Overall, the results highlight that user
expectations focus primarily on core performance and transparency rather than on minor usability aspects. Different to other areas, Danish drivers do not differ too much from the other
ones – they state similar areas for improvement.
Top 5 aspects to improve performance of ACC
Adaptive Cruise Control (ACC) is seen less crucial, and satisfaction rates are much above
LKA. However, even ACC can be optimized. Strongest ACC improvement areas concern:
1.
2.
3.
4.

system precision (33.0%),
followed by function in all-weather situations (30.3%),
function in all road situations (27.3%),
traceability of system actions (25.6%), and

5. personalization (23.8%).
Other factors such as reliability (23.1%), and reaction speed (19.4%) are of medium importance, while operation (16.0%) and sense of security (10.6%) are least frequently mentioned. This indicates that users primarily demand accuracy, transparency, and robustness
across conditions, whereas usability and perceived security are seen as comparatively less
critical issues. As mentioned before, Denmark often shows different results but not here – the
requirements are similar to the other countries. However, different to the other countries “traceability of system actions” is not an issue for them – just 9.3% see immediate potential to improve.
Across markets, the most pressing improvement areas for ACC are linked to precision, adaptability under varying conditions, and system transparency, while usability-related aspects such
as operation and perceived security are of lower priority.

6.1.7 Future innovation: customer attitudes towards autonomous mobility
Actual trust in ACC and LKA is linked to the believe in future technologies. Across all markets,
belief in autonomous mobility remains moderate-low, with 41.9% of respondents expressing
confidence in this technology (see figure 19).
The highest level of approval is observed in Germany (50.8%), which stands out clearly above
the total average. In contrast, Austria (32.3%), Italy (32.5%), and Denmark (33.9%) report

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considerably lower values, indicating pronounced skepticism. Switzerland (41.3%) and respondents from other countries (43.2%) are closer to the overall mean, reflecting more balanced attitudes. It is remarkable that Danish drivers are more critical about autonomous mobility while their satisfaction with ADAS is significantly higher.
Taken together, the findings suggest that while there is a certain openness towards autonomous mobility, acceptance remains limited across all markets, and even in the most optimistic
context, a substantial proportion of respondents continue to express reservations.

Figure 19: Believe in autonomous mobility across countries
Concerns about autonomous driving are especially about:
1. Liability issues (63.6% of total sample), and
2. Safety issues (54.7% of total sample).
Data protection problems are not so important (mentioned by 43.3%) compared to the first two
major aspects.
If autonomous mobility were to become available, user preferences would primarily concentrate:
1. on highway trips (69.5%),
2. for shuttle services (63.6%),
3. for commuting to work (47.3%), and
4. for city traffic (44.6%).

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These results suggest that respondents associate autonomous driving above all with longerdistance travel on highways. It is linked to the survey result that road situation is influencing
ADAS performance.

6.1.8 Summary of results for consumer survey
The immense set of survey data can be summarized in our model below. The model illustrates
the key influencing factors and their interrelations, showing how performance in precision, perceived security, stress reduction, and system intervention impacts satisfaction, trust, and ultimately system use. By mapping these connections, the model also highlights the central fields
of action that need to be addressed in order to improve acceptance und usage of ACC and
LKA.
ACC survey summary
The model demonstrates that Adaptive Cruise Control (ACC) is positively evaluated by users,
with key strengths identified in reliability, perceived safety benefits, and comfort. These factors
lead to a high overall satisfaction level (mean score: 4.04), which in turn translates into a high
level of trust (mean score: 3.76).
This positive evaluation for ACC is reflected in actual usage behavior: 69% of drivers report
using ACC frequently or all times, while only 12.8% actively switch the system off. Moderate
complaints about system intervention are observed, yet they do not substantially undermine
the positive assessment. Overall, the results indicate that ACC achieves both high satisfaction
and trust, which are closely linked to widespread and consistent system use.

Figure 20: Summary of core survey results for ACC
LKA survey summary
Deficits for LKA in precision, perceived security, stress reduction, and system intervention directly undermine user satisfaction (total score 3.2), as expectations for safety and comfort remain unmet. More than 50% complain about unpleasant intervention of LKA – which increases
the stress level and decreases trust. To conclude - moderate satisfaction translates into even

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lower trust levels (trust mean score = 2.80), with nearly one third of drivers not using the system
(30.8%) and 30.7% actively switching LKA off and 29.7% ignore the assistance.

Figure 21: Summary of core survey results for LKA
Overall reflection: driver perceptions and attitudes on ACC and LKA
The comparative analysis of Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC)
reveals substantial differences in user perception, satisfaction, trust and acceptance. While
ACC is characterized by high satisfaction (4.04), strong trust (3.76), and frequent use (69%
regularly, only 12.8% switch-off), LKA shows clearly lower satisfaction (3.2), even lower trust
(2.8), and a high share of non-use (29.7%) or active switch-off (30.7%).
The underlying reasons diverge as well: for ACC, strengths lie in reliability, safety benefits, and
comfort, with only moderate complaints about unpleasant intervention by the system. For LKA,
persistent deficits in precision, reliability in different driving situations, stress reduction, and
transparency lead to unmet expectations regarding safety and comfort, thereby undermining
trust and reducing actual usage.
LKA’s functionality outside of highways is often criticized. However, it is precisely on country
roads that the greatest safety potential could be realized. Unfortunately, it is on country roads
that its functionality is criticized. In summary, LKA often does not work in all driving situations
in a proper way.
When considering the influence of drivers’ behavior and attitude on satisfaction with the systems, hypotheses can also be derived here. The data indicates that tech-savvy users who
engage intensively with the systems are more satisfied. This may be because certain system
interventions are easier to understand. Ergo – users must be informed, trained, and also made
aware of the limitations of the systems. This adjusts their expectations and understanding,
which in turn has a positive influence on satisfaction.

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Overall reflection: country results and highlights
Across the given countries, ACC achieves high satisfaction, trust, and frequent use, while LKA
is marked by deficits that limit trust and lead to frequent non-use or deactivation. The core
country pattern is consistent: national differences mainly concern the intensity of criticism, not
the overall direction of results.
•

Denmark: results of the survey in Denmark differ greatly from those in other countries
(these, in turn, show relatively high levels of homogeneity). Users in Denmark are better
informed, more satisfied, and also have greater trust in the systems. As a result, usage
is also much more intense in Denmark – for both, ACC and LKA.

•

Germany and Switzerland: show relatively slightly higher satisfaction and trust with
both systems (above the average scores), though the gap between ACC (positive) and
LKA (critical) remains pronounced.

•

Austria and Italy: consistently more skeptical, Austria especially regarding LKA, with
below-average trust and higher non-use.

Why are Danish people so much more satisfied? The study tries to explore this question with
following assumptions:
•

Danish users engage more intensively with ADAS (pre and after car purchase) and
learn the systems more proactively. The proportion of people who do not engage with
the systems is significantly smaller compared to other countries. At the same time,
people are generally more interested and open to ADAS. This point might have a very
strong impact on ADAS acceptance and satisfaction. What can the industry lean from
that? Users need to be informed, to be trained and convinced about the benefits of
ADAS. However, the principal issues often stay the same, but on a more positive level.

•

With almost 95%, the share of male participants is very high – at the same time we
identified a gender impact on satisfaction (for all countries). This is slightly influencing
the results as well.

•

A better road infrastructure might impact the results as well – especially due to the fact,
that country roads with insufficient road markings impacting satisfaction scores badly.

•

FDM promotes safety very strongly, e.g. in each car test ADAS is rated separately and
the importance of ADAS for safety is underlined very strongly. In Denmark there is a
very pro-active communication for ADAS.

•

The car fleet is relatively young and cars are often high-end equipped. Due to the tax
system combined with a newer car-fleet Denmark now has a strong market share of
EVs.

These results are vital for industry and stakeholder implications and need for actions. It will be
considered in the following chapters.

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6.2

ADAS penetration study

6.2.1 German ADAS penetration

Rel. Penetration

Annual penetration of selected ADAS in
new vehicles (2001 – 2024)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2001

Fleet penetration
ADAS 2024
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

2006

2011

2016

2021

Year of Registration
ACC

LKA

Parking automation

Figure 22: Development of new-vehicle ADAS penetration in Germany

Fleet penetration

Annual vehicle on-street fleet
penetration (2001 – 2024)

Fleet Penetration
of ADAS 2024

100%

100%

90%

90%

80%

80%

70%

70%

60%

60%

50%

50%

40%

40%

30%

30%

20%

20%

10%

10%

0%
2001

2006

2011

2016

2021

0%
ACC

LKA

PMA

Year of Observation
ACC

LKA

PMA

Figure 23: Development of on-street fleet penetration in Germany

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6.2.2 Italian ADAS penetration

Rel. Penetration

Annual penetration of selected
ADAS in new vehicles (2001 – 2024)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2001

2006

2011

2016

2021

Factory penetration
of ADAS 2024
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Year of Registration
ACC

LKA

Parking automation

Figure 24: Development of new-vehicle ADAS penetration in Italy

Fleet penetration

Annual vehicle on-street fleet
penetration (2001 – 2024)

Fleet Penetration of
ADAS 2024

100%

100%

90%

90%

80%

80%

70%

70%

60%

60%

50%

50%

40%

40%

30%

30%

20%

20%

10%

10%

0%
2001

0%
2006

2011

2016

2021

ACC

LKA

PMA

Year of Observation
ACC

LKA

PMA

Figure 25: Development of on-street fleet ADAS penetration in Italy

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6.3

European traffic safety statistics
Fatalities per million pop.
In 2023
60

Year
Denmark

Germany

Italy

Hungary

Switzerland

EU-27

Fatalities per milion pop. in 2023

80
70
60
50
40
30
20
10
0

2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023

Annual fatalities per million pop.

Development of annual road
fatalities per million

50
40
30
20
10

0

Austria

Figure 26: Traffic road safety related outcomes in selected focus countries in Europe

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7

Discussion and insights

7.1

KPI reports

7.1.1 Safety

Figure 27: KPI dashboard for safety

7.1.2 Safety perception

Figure 28: KPI dashboard for safety perception

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7.1.3 Reliability

Figure 29: KPI dashboard for reliability

7.1.4 Reliability perception

Figure 30: KPI dashboard for reliability perception

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7.1.5 Future readiness

Figure 31: KPI dashboard for future readiness

7.1.6 User Satisfaction and UX

Figure 32: KPI dashboard for UX and satisfaction

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7.1.7 Acceptance and Usage

Figure 33: KPI dashboard for acceptance and usage

7.2

European report

Figure 34: KPI dashboard for European countries in focus

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7.3

Predictions on DCAS step 3

Predictions on DCAS step 3 are based on the results conducted during the customer survey.
Trust in DCAS like LKA only 45,6% (interpreted as poor) and ACC with 61,66% (interpreted as
moderate) do not lead to the assumption that an even higher degree of autonomy will be
trusted in by the users. Active usage of DCAS step 2 systems is also poor for LKA (46,7) and
moderate for ACC (66,6%). Even with the availability of ACC and LKA systems users prefer to
switch-off the assistance system moderately often LKA (31,7 %) and ACC not often (13,5%).
According to the data retrieved from the survey, the predictions for a higher degree of automation are not going to grow in a positive direction.
Indicators regarding future acceptance of future autonomy are also poor (42,6%) analyzed
from the survey data. Respondents also highlighted their areas of concern for future autonomy
which are as follows:
Liability concerns in the event of accidents (63,8%)
Safety concerns (54,3 %)
Data protection (43,2%).
DCAS step 3 is placed between the survey responses of DCAS step 2 and future autonomy
as shown in the picture below. Predictions are extrapolated from the survey and result in poor
trust, low active use and average to high switch-off rates. According to literature the future
autonomy and full ADAS deployment could lead to a beneficial effect on road safety. This
statement leaves us with a potential safety increase which cannot be exhausted according to
the predictions given.

Figure 35: Overview on predicted trajectory for future stages of autonomy

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8

Policy recommendations

Figure 36: Summary of policy recommendations

8.1

Information and Awareness

8.1.1 Awareness campaigns
Based on survey results, we observe a clash between the safety potential and the perceived
safety that participants reported. While low rates of perceived safety can be attributed to factors
like insufficient reliability, we propose that a lack of public ADAS awareness is responsible for
the relatively low levels of perceived safety.
In a worst-case scenario, an uninformed public might be skeptical of ADAS and in turn avoid
ADAS-equipped vehicles or even new vehicles entirely, considering regulations like (EU)
2019/2144 or their successors. Furthermore, extrapolating from survey results, a relatively high
amount of survey respondents reporting switching off their ADAS system could indicate that
without an appropriate ADAS campaign, future systems such as proposed under DCAS step3 might face similar fates.
For this, we propose considering EU-wide awareness campaigns on ADAS, focusing on their
safety benefits and explaining their limitations. We propose that a better-informed public will in
turn be more open to ADAS use and be less likely to misuse systems, avoiding issues like
overreliance. Information campaigns on ADAS might also reduce switch-off rates and increase
the rates of drivers willing to acquire ADAS-equipped vehicles. A cost-benefit analysis for such
an intervention is yet to be made.

8.1.2 ADAS as part of formal driving education
Accounting for the increasing number of ADAS systems on the road and a full fleet penetration
in the future, usage of and interaction with ADAS is increasingly becoming a safety-relevant

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element of the driving task. We therefore propose that newly learning drivers should be as
proficient in the usage of ADAS as in any other driving-related task.
We predict that a generation of drivers that learn and practice driving with ADAS from the
beginning of their education will be less reluctant to use it, possibly leading to higher usage
and lower switch-off rates in the long-term perspective. Not including ADAS in the formal driving education would risk having new drivers unfamiliar with these systems and lose the potential on increased safety that these systems pose.
Therefore, we propose establishing the necessary skillset required to operate ADAS. The elements of the skillset could be developed in terms of a study that would consider expert interviews, user surveys, and regulatory analysis. The skillset can then be transferred to be applied
in formal driving education on an EU scale and be taught both in theory and in practice.

8.1.3 Customer briefing after vehicle acquisition
The event of acquiring a vehicle poses many challenges to drivers. New vehicles are equipped
with new technology that the individual driver might not be used to from previous vehicles. This
is especially the case for ADAS. In this context only 38,3% of the total sample reported that
they were familiarized with ADAS by the car dealer – in Germany, Italy and Luxembourg this
share is even lower. Denmark as the country with the best satisfaction scores shows again
special results in this context – e.g. just 7.5% state “no conscious familiarization with ADAS”.
This is by far the lowest share in our sample. It indicates that there is a lost potential to raise
awareness of ADAS and reducing usability issues.
Our proposition to tackle this is that either via a policy-driven approach, or by an industry driven
joint effort, a standard ADAS briefing is developed and offered to new customers unfamiliar
with ADAS or unfamiliar with the usage of ADAS on a specific vehicle. Similarly to our proposed
ADAS skillset for formal education, the briefing could be based on three columns: A general
briefing on ADAS (theory), a briefing on ADAS operation on the specific vehicle (theory), and
then a test drive with the specific vehicle (practice).

8.2

Information and Data

8.2.1 Improving quality and availability of data on ADAS
All efforts to improve traffic safety rely on the principle of interventions and measurement of
intervention outcomes. A higher quality of outcome data allows better interventions. In the case
of ADAS, the EU and its member states are intervening in traffic safety with ADAS, while the
actual public effects can only be measured in limited ways, such as field studies.
We propose that public outcomes should be measured in terms of relevant KPIs and an associated model of influences. One of the most important KPI areas, safety, is lacking a proper
set of measurement values available in the member states.

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These KPIs include:

•

Public reported ADAS penetration quotas: Developing a reporting system that allows
a clear overview of the type and amount of ADAS present in fleets. This number’s
quality is also a foundation for improving models to validate intervention outcomes.

•

Reporting of ADAS equipment in case of traffic accidents: standard police reports
typically don’t contain reporting on the specific equipment of ADAS in the vehicles
involved in traffic accidents. Furthermore, there is no standard procedure to determine
involvement of ADAS in accidents.

•

Transparency of manufacturer data: manufacturers gather, process, and record
ADAS data on a big data scale. This information, however, is only industrially available,
not homologated, and accounts for no system whereby independent authorities can
use data to infer conclusions for policy-driven measures.

8.2.2 Public availability of user studies with ADAS
While a wide set of studies regarding certain aspects of ADAS acceptance are conducted,
these studies often follow individual methodologies and are therefore not comparable over
time. If usability and the role of the human operator (user) is to be considered, then a reproducible study design is missing.
We therefore propose for organizations advocating consumer-rights or bodies involved in the
safety rating of vehicles to regularly conduct user studies to examine the current state of user
satisfaction with systems available on the market in a comparative, benchmark-like approach.
Such a test could reveal insights on both usability and functional aspects, highlighting areas
where development is needed, raising transparency on the comparative performance of different systems for prospective customers and ultimately incentivize developers to improve systems.

8.2.3 Developing a set of measures for wider ADAS availability
While the successful future of autonomy depends primarily on user acceptance and ADAS
quality, an important factor to consider is infrastructure. We propose that an infrastructure
adapted to the needs of ADAS should be promoted.
The basis of infrastructure – related undertaking is cost-intensive in nature and resources limited. It is therefore useful to conduct a study to identify KPIs to prepare infrastructure for an
increasingly autonomous future, investigating areas for improvements by conducting expert
interviews and examining literature. Such an analysis could include cost-effectiveness analysis
and bring forward a detailed action plan for promoting autonomous mobility from a infrastructure – driven perspective.

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8.3

Improving ADAS quality

8.3.1 Policy-driven standards on reliability and ODD
ADAS available on the market have a high degree of differences between systems of different
manufacturers. Among other effects, this affects also the available ODD by system and the
general system reliability.
We propose that systems with low reliability or insufficient ODD are detrimental to the longterm acceptance of ADAS. Similarly, systems with inconsistent ODDs force the user to readapt
from one system’s ODD boundaries to another, potentially causing dissatisfaction, a higher
mental workload, and ultimately refusal to use a system.
To avoid this, we argue that in regular accordance with technological development a minimum
standard for ODD by ADAS given is established. The minimum standard ODDs serve as framework upon which vehicle developers can extend their system’s capabilities. Furthermore, a
policy-driven testing procedure should be enforced that test the reliability of ADAS within the
currently defined ODD standard. 

[The evaluation harness truncated this reference: showing the first 120000 of 136820 characters.]
</reference>

<statements>
1. Comparative negligence, strict product liability, and consumer protection law together govern how fault is allocated between drivers and ADAS providers.
2. Under the SAE J3016 taxonomy, most systems currently deployed in consumer vehicles are Level 1 or Level 2—providing assistance with either steering or speed, or both, while requiring the human driver to remain responsible and able to take over at any time.
3. These standards embed technical requirements such as minimum sensing ranges, lane coverage, and minimum-risk maneuvers in case of system faults or driver non-intervention, implicitly assuming shared responsibility between human and machine.
4. Across jurisdictions, a baseline principle holds that Level 2 ADAS systems are driver-assistance tools, not substitutes for human drivers, meaning drivers remain legally responsible for safe vehicle operation. Regulatory frameworks such as UNECE’s DCAS and ALKS rules state that the driver must be ready to take over at any time and remains responsible, even when systems perform sustained longitudinal and lateral support. Practice-oriented guidance in California and Florida likewise underscores that drivers are usually expected to stay in control, and inattentiveness will typically factor into negligence analysis.
5. Supervised autonomy (Level 2 and some Level 3 arrangements), where computer drivers perform dynamic tasks but humans must supervise and be ready to take over, leading to shared or shifting liability depending on system engagement and alerts.
6. Technical regulations like UNECE R157 and R171 should be complemented with detailed standards for driver-monitoring systems, takeover request design, and HMI cues that directly influence liability assessments.
7. These measures would better reflect collaborative driving realities, promote safer ADAS deployment, and provide clearer ex ante expectations for drivers, manufacturers, and insurers as intelligent transportation systems continue to evolve.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.