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>
FDCC Annual Meeting 2026
Lisbon, Portugal

Asleep at the Wheel: Assessing Liability and Risk when Technology is
in the Driver’s Seat
INTRODUCTION:
When machines make decisions traditionally made by humans, who bears responsibility when
something goes wrong? From advanced driver-assistance systems (ADAS) to fully automated
driving systems (ADS), technology increasingly performs tasks once reserved exclusively for
human vehicle operators. Thus, what once involved relatively straightforward negligence
claims against human drivers now presents complex, multi-party disputes potentially involving
manufacturers, software developers, fleet operators, insurers, and technology vendors.
As vehicles increasingly rely on artificial intelligence, machine learning, sensors, and over-theair software updates, the traditional distinction between “driver error” and “product defect”
are less clear. Existing tort negligence and liability theories remain relevant, but they are
increasingly strained by technologies that blur the line between human conduct and machine
decision-making. As vehicles become more technologically autonomous it is less certain who
ultimately bears the financial and legal consequences when technology fails.
AUTONOMOUS VEHICLE TECHNOLOGY:
An autonomous vehicle (“AV”) is a vehicle equipped with technology capable of performing
some or all driving functions without continuous human input. These systems rely on
combinations of:
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Artificial intelligence (AI)
Machine learning algorithms
Cameras and computer vision systems
Radar and LiDAR sensors
GPS and mapping technologies
Vehicle-to-vehicle (V2V) communication
Vehicle-to-infrastructure (V2I) communication
Real-time data processing systems

Most modern autonomous systems continuously collect and analyze environmental data in
order to make operational driving decisions such as steering, braking, acceleration, lane
positioning, obstacle avoidance, and route navigation.

Autonomous vehicle technology exists along a spectrum developed by the Society of
Automotive Engineers (SAE). These levels of automation range from Level 0 (no automation) to
Level 5 (full automation). Understanding the level of automation is important is understanding
how liability exposure can change.
Levels of Automation:
Level 0 – No Automation. The human driver performs all driving tasks. Technology may provide
warnings but does not actively control vehicle functions. (e.g. blind spot monitoring, lane
departure warnings)
Level 1 – Driver Assistance. The vehicle can assist with either steering or acceleration/braking,
but not both simultaneously. The driver remains fully responsible for vehicle operation. (e.g.
adaptive cruise control, lane centering assistance)
Level 2 – Partial Automation. The vehicle can simultaneously control steering and
acceleration/braking under certain conditions, but continuous human monitoring is still
required. (e.g. highway autopilot systems, traffic jam assist systems, advanced lane centering
technologies)
Level 3 – Conditional Automation. The vehicle can independently operate under certain
conditions without constant human monitoring, but the drive must be available to intervene
upon request.
Level 4 – High Automation. The vehicle can perform all driving functions within designated
operational domains without human intervention.
Level 5 – Full Automation. The vehicle performs all driving functions under all conditions
without any human involvement.
These classiﬁcations distinguish truly automated driving systems (ADS) from Advanced DriverAssistance Systems (ADAS).
ADAS are technologies designed to assist human drivers—not replace them. ADAS systems
support driving functions such as steering assistance, braking, speed, lane positioning, parking,
and collision avoidance. ADAS vehicles still require constant driver oversight, attentiveness and
intervention.
Unlike ADAS systems, ADS vehicles are intended to perceive environmental conditions
independently, make operational driving decisions, and execute vehicle control functions
without constant driver oversight. At the higher levels of automation, the system—not the
human—becomes the primary operational decision-maker.

Most ADAS-equipped vehicles currently operate at SAE Levels 1 or 2 automation. True
autonomous operation (ADS) generally begins at SAE Level 3 and expands signiﬁcantly at Levels
4 and 5.
ADAS (Level 2)

ADS (Levels 3–5)

Examples:

Examples:

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Tesla Autopilot/FSD
Ford BlueCruise
GM/Cadillac Super Cruise

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Waymo robotaxis
Mercedes Drive Pilot
Cruise autonomous vehicles
Zoox test fleet

RECENT LITIGATION AND LEGISLATION:
Although there is a growing number of much more highly publicized incidents and crashes
involve ADS vehicles such as Waymo, currently the majority of reported AV crashes and
resulting litigation involve Level 2 systems; however, there is still very limited guidance to date
on how fault should be determined.
On August 1, 2025, in Benavides v. Tesla, Case No. 21-cv-21940, in the U.S. District Court for the
Southern District of Florida, a jury found Tesla partially liable for a fatal crash involving its
“Autopilot” system and awarded approximately $243 million in compensatory and punitive
damages. The accident occurred while the driver was operating a Tesla Model S with Autopilot
engaged. The vehicle failed to stop at a T-intersection and fatally struck one pedestrian and
seriously injured another. Both the driver and Tesla were named as defendants. The defendant
driver admitted he was distracted by his phone at the time of the crash and settled out. Tesla
denied responsibility. Plaintiffs argued Tesla’s Autopilot system could be activated in unsafe
conditions; that the driver monitoring measures were insufficient to assure a driver remained
engaged; and that Tesla’s marketing overstated Autopilot’s capabilities and encouraged
overreliance on the system.
The jury agreed that Tesla was independently negligent and apportioned fault between the
driver and Tesla, assigning approximately one-third of the liability to Tesla, making this one of
the first major jury verdicts imposing substantial liability on the vehicle manufacturer based on
failure to warn and design defects based, in part on consumer expectations. The case is also
notable for the award of significant punitive damages due to Tesla’s statements and marketing
materials that jurors clearly believed was evidence of corporate indifference to driver safety.
Until this case, Tesla and other manufacturers had successfully defended claims taking the
position that the driver alone bears responsibility for Level 2 automation systems. Post trial
motions have been filed and further appeals are expected.

Accidents involving AVs are also being scrutinized by state and federal regulatory and
administrative authorities for safety concerns, but there is still limited focus on fault allocation
for accidents.
At about the same time the verdict in Benavides was rendered in August 2025, a federal class
action alleging deceptive marketing of Tesla’s “Full Self Driving” (FSD) package was certified in
California in In re Tesla Advanced Driver Assistance Systems Litigation, Case No. 22-cv-05240, in
the U.S. District Court for the Northern District of California. The certiﬁed class covers California
residents who purchased or leased a Tesla and paid separately for the Full Self Driving package
between May 19, 2017 and July 31, 2024. and is still in the early stages.
Most recently on March 31, 2026, the National Transportation Safety Board (NTSB) completed
an investigation into two fatal crashes in 2024 and concluded that Ford Motor Company’s
hands-free partial automation system, Blue Cruise, contributed to both crashes and made
recommendations to the US Department of Transportation and Ford calling for stronger federal
guidelines and performance standard, crash data recording and automatic notification
requirements, improved driver monitoring systems to detect distraction and changes to the
Ford’s Blue Cruise system to reduce excessive speeding and improve driver attention.
In early May 2026 the National Highway Traffic and Safety Administration (NHSTA) opened a
probe into 16 crashes involving Avrides’s autonomous delivery vehicles and robotaxis which
operate on the Uber platform in Texas concerning allegations of aggressive/assertive behaviors
and failure to avoid obstacles.
Several states, such as Colorado, Mississippi and California, have passed legislation governing
various aspects of autonomous vehicles. However, Utah Senate Bill 292 was signed into law by
the Governor on March 23, 2026 and provides the most detailed attempt at legislating
responsibility for motor vehicle accidents involving AVs. The bill generally restricts actions
against the ADS (Level 4 or 5) manufacturer to two paths. The ﬁrst is the special "driverless
operation liability", an exclusive remedy for claims against the owner and dispatcher which is
limited to $100,000. The plaintiﬀ must prove that the ADS was engaged, and that the ADS was
the proximate cause of the injury. Larger claims would have to pursue a product liability claim,
which would be limited to $1million, but the plaintiﬀ would have to prove that a reasonable
alternative design exists, and that ADS use causes more injuries than human drivers. The law
became eﬀective on May 6, 2026. Many other states and the federal government also have
numerous proposed legislative changes that are under consideration.
EVOLVING THEORIES OF LIABILITY AND RISK:
Automobile litigation has historically centered only on driver conduct and traditional tort
negligence principles. As automation expands, liability analysis and exposure shifts from solely
human conduct toward system performance and technological reliability and liability may turn
less on the collision itself and more on the system architecture, software design decisions,
human-machine interaction, and possible consumer expectations created by marketing and

branding. Therefore, when an AV is on the road, liability may be shared or entirely transferred
to many potential parties in addition to the driver such as manufacturers, software developers,
fleet operators, component parts manufacturers and suppliers, or third party data providers.
For insurers and defense counsel, AV claims may no longer resemble traditional motor vehicle
accidents. Instead, they are evolving into hybrid cases also involving products liability,
cybersecurity, and technology litigation.
Moreover, new coverage questions and challenges are presented as parties seek to understand
what coverage may be implicated and what policy provisions or exclusions may apply,
potentially under automobile, general liability, cyber, product liability, or other coverage.
Traditional comparative negligence frameworks become more complicated when autonomous
systems share operational control with human drivers. In some cases, defense counsel may
argue that inattentive drivers remain the primary proximate cause. Plaintiffs, however, may
contend that the system itself induced foreseeable driver complacency. Additionally, a major
challenge involves distinguishing between human and machine responsibility. Human drivers
remain susceptible to distraction, fatigue, intoxication, and poor judgment. Autonomous
systems, by contrast, may fail because of programming limitations, sensor inaccuracies, or
algorithmic bias.
The recent verdict in Benavides, illustrates that this tension has already emerged in litigation
involving ADAS where manufacturers market features with names implying full autonomy
despite requiring human attention. Courts may increasingly examine whether marketing
practices create unreasonable consumer reliance on automation.
AVs also present unique challenges because software behavior can evolve after deployment
through updates and machine learning. Unlike static mechanical components, AI systems may
adapt unpredictably over time.
For example, if an autonomous driving system misidentifies a pedestrian or fails to respond
appropriately to road conditions, plaintiffs may argue that the software was defectively
designed. Manufacturers, however, may contend that the driver ignored instructions requiring
continued supervision.
Cybersecurity and Data privacy risks are also present. Autonomous vehicles depend heavily on
interconnected software systems, cloud computing, GPS, and wireless communication. This
dependence creates substantial cyber threats such as remote hacking of vehicle controls, GPs,
spoofing, ransomware attacks on transportation systems, GPS spoofing, or data breaches.
Further insurance companies may gain access to extensive driving data, enabling more precise
evaluation of risk but also raising privacy concerns.
Cybersecurity failures could expose manufacturers and service providers to negligence claims if
reasonable safeguards were not implemented. Additionally, questions arise regarding
ownership and privacy of the vast amounts of data generated by autonomous vehicles.

Governments and regulators increasingly recognize cybersecurity as essential to transportation
safety. However, regulation of autonomous vehicles remains fragmented and inconsistent.
Federal, state, and international authorities continue to develop standards governing testing,
deployment, and safety compliance. Regulators face the difficult task of balancing innovation
with public safety. Overregulation may slow technological progress, while insufficient
regulation could expose the public to unacceptable risks.
Autonomous vehicles also raise ethical concerns extending beyond traditional legal analysis. AI
systems may face unavoidable crash scenarios requiring programmed decision-making about
harm allocation. Although these dilemmas are statistically rare, they highlight the broader
societal implications of delegating life-and-death decisions to machines.
Finally, traditional auto insurance focuses primarily on driver behavior. However, autonomous
vehicle claims present significant insurance coverage uncertainties that existing policy language
may not adequately address and auto insurers will likely seek to push risk to the manufacturer’s
insurance, cybersecurity coverage, or other policies. Insurers may also seek to revise policy
language such as AV operation endorsements or exclusions or AI and technology or software
related exclusions or limitations.
ACCIDENT INVESTIGATION AND LITIGATION CHALLENGES:
Autonomous vehicle claims and litigation introduce substantial discovery complexities.
Critical evidence may include:
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Vehicle telemetry data
Event data recorder (“black box”) information
Sensor recordings
Internal software logs
AI decision-making pathways
Over-the-air update histories
Driver monitoring system data

However, these types of data create potentially thorny discovery and admissibility issues.
Disputes over preservation or potential spoliation of evidence are possible. Potential issues
involving authentication or protection of trade secrets and proprietary software and source
codes are also likely. As litigation over autonomous vehicles develops, courts will increasingly
confront disputes over discoverability of algorithmic decision-making processes and the extent
to which manufacturers must disclose proprietary AI systems.
Early preservation of data and engagement of experts such as an accident reconstructionist,
software engineers, human factors specialists, cybersecurity professionals, and even coverage
counsel is highly likely and frequently critical for insurers and defense counsel to effectively
assess and defend AV claims and litigation.

CONCLUSION:
Autonomous vehicles claims are changing the landscape of automobile accident claims. As the
technology provides more features and more active control over the operation of a vehicle, the
liability analysis becomes murky. Litigation increasingly resembles traditional product defect
litigation rather than ordinary automobile negligence case including theories regarding
defective design and failure to warn. It encompasses not only whether there was negligent
vehicle operation but also determinations regarding the role of new or emerging technology
and the potential fault of new and different parties such as manufacturers, programmers,
designers and distributors.
Uncertainty abounds. Who is covered? Is the loss an auto accident or a product failure? Who
was the driver? Does software constitute a product? Could liability rest with a rogue agent?
Because autonomous vehicles depend heavily on interconnected software and wireless
communications substantial cyber exposure may exist such as remote hacking, AI manipulation,
data breaches, or ransomware attacks, leaving further uncertainty about who is at fault when
technology takes the wheel.
</reference>

<statements>
1. Comparative negligence, strict product liability, and consumer protection law together govern how fault is allocated between drivers and ADAS providers.
2. The report concludes with proposed regulatory guidelines: (1) explicit statutory allocation rules keyed to automation mode and transition periods, (2) mandatory data retention and disclosure for accident reconstruction, (3) harmonized standards on driver monitoring and takeover requests, and (4) doctrines recognizing overstatement of autonomy as a form of misrepresentation or unfair practice.
3. In ADAS crashes, defense counsel often argue that inattentive drivers remain the primary proximate cause, while plaintiffs contend that system design foreseeably induces complacency or overtrust.
4. Under strict liability, plaintiffs typically must show that a defect made the product unreasonably dangerous, that the defect existed when sold, that the manufacturer created or sold the product, that the defect directly caused the injury, and that the product was used in a reasonably foreseeable manner without overriding safety features.
5. Product defect, failure-to-warn, and misrepresentation claims are particularly salient when manufacturers overstate autonomous capabilities or downplay limitations.
6. Design decisions that create foreseeable automation complacency, such as unclear takeover requests, inadequate driver-monitoring, or misleading HMI cues, may also be evaluated under negligence or strict liability.
7. Comparative negligence and multi-cause tort theories allow courts to apportion liability between drivers and manufacturers based on relative contributions.
8. Scholarship on partial autonomy argues that modern supervised systems require collaborative driving, and drivers should not bear full liability when system design reasonably engenders misplaced trust.
9. This approach aligns liability with realistic human behavior in complex socio-technical systems and incentivizes safer interface design.
10. Current law generally treats Level 2 ADAS as driver-assistance tools, preserving primary driver responsibility, but product liability, comparative fault, and consumer protection frameworks increasingly recognize manufacturer and system responsibility where defects, design limitations, or misleading marketing contribute to crashes.
</statements>

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