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Policy and Society

ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rpas20

Law and tech collide: foreseeability,
reasonableness and advanced driver assistance
systems
Tania Leiman
To cite this article: Tania Leiman (2021) Law and tech collide: foreseeability, reasonableness
and advanced driver assistance systems, Policy and Society, 40:2, 250-271, DOI:
10.1080/14494035.2020.1787696
To link to this article: https://doi.org/10.1080/14494035.2020.1787696

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Group.
Published online: 15 Aug 2020.

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POLICY AND SOCIETY
2021, VOL. 40, NO. 2, 250–271
https://doi.org/10.1080/14494035.2020.1787696

ARTICLE

Law and tech collide: foreseeability, reasonableness and
advanced driver assistance systems
Tania Leiman
Dean of Law, Flinders University, Adelaide, Australia
ABSTRACT

KEYWORDS

Recently, many scholars have explored the legal challenges likely to be
posed by introduction of automated and autonomous vehicles.
Minimal attention has focused on the legal implications of advanced
driver assistance systems (ADAS) in vehicles already currently available.
These can warn of external dangers, monitor driver behavior and
control how a vehicle brakes, accelerates, maintains speed or position
on the road. The dynamic driving task is no longer reliant simply on the
physical interaction of human driver with that vehicle. Instead, the
vehicle may act apart from human direction as it senses other objects
in the immediate environment or monitors the human driver’s beha­
vior or biometrics. These technological tools, which reduce the oppor­
tunity for human error, can be described as augmenting human
driving capacity. Increases in safety promised by ADAS, arguably
already evidenced by data, may require a reassessment of the risks
posed by ‘un-augmented’ human drivers, what is now foreseeable
given the data generated by ADAS and wearable driver-monitoring
technology, and whether ‘un-augmented’ driving is any longer
a reasonable response to that risk.

Automated vehicles; liability;
ADAS; foreseeability

Introduction
Broader issues of governance of artificial intelligence and autonomous systems inevitably
are worked out in the detail of regulatory regimes, including legislation passed by
parliaments and its subsequent interpretation and application by courts. In their deci­
sions, arguably the practical outworking of governance, judges ‘make the law’, yet must
‘grapple with the fundamental problem of determining the limits of judicial law making
responsibility’ particularly when faced with the ‘novel and . . . difficult questions gener­
ated . . . by astonishing scientific and technological advances and the great social and
economic changes wrought by globalisation and the spreads of international human
rights’ (Sackville, 2001). Adopting a narrower perspective to those regarding theoretical
approaches to governance outlined earlier in this issue by Ulnicane et al. (2020), Radu
(2020) and Gahnberg (2020), this paper focuses on application of regulation in the
context of automated vehicles. In this context, automation, machine learning and
algorithmic decision-making transport humans and cargo, sometimes colliding and
causing damage. These technological agents ‘perceive’, ‘decide’, act and finally move,
CONTACT Tania Leiman
tania.leiman@flinders.edu.au
University, GPO Box 2100 Adelaide, South Australia 5001

College of Business, Government & Law, Flinders

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://
creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium,
provided the original work is properly cited.

POLICY AND SOCIETY

251

impacting not only a digital environment but also a highly complex physical one. This
physical environment demands abstract theoretical considerations about governance are
outworked in highly specific rules and practical decision-making (Gahnberg, 2020). The
law has been used historically as a policy lever to pursue social goods such as safety,
penalise the taking of unacceptable risks and either encourage or stifle emerging tech­
nological innovations. This paper will consider how common law concepts such as
reasonableness, practicability and foreseeability and their use in apportioning liability
might be applied to the challenges posed by automated vehicle technologies already in
use, and how this can inform future approaches to regulation of highly and fully
autonomous vehicles.
Recently many scholars have explored the legal challenges likely to be posed by
introduction of automated and autonomous vehicles – described as ‘a qualitatively
distinct affordance’ (Calo, 2019, p. 86). Vehicles with conditional, high and full levels
of automation are projected ‘to result in [even more] significant community benefits
including reduced road trauma and increased mobility, productivity and environmental
efficiencies’ (National Transport Commission, 2017; Haratsis, 2019), but they are not yet
widely available, and it remains to be seen whether performance will live up to prediction.
A variety of advanced driver assistance systems (ADAS) are already deployed in
vehicles in use on our roads. These can warn of external dangers, monitor driver behavior
and provide additional control of braking, acceleration, speed or position on the road. ‘At
the highest level of intervention, ADAS either take action independently or override the
action of the driver’ (Lindgren, Chen, Jordan, & Zhang, 2008), and ‘represent an evolu­
tion in vehicle sensing, intelligence and control that will ultimately lead to self-driving
cars’ (Estl, 2015, p. 2). The dynamic driving task no longer relies simply on the physical
interaction of human driver with that vehicle. Instead, the vehicle may act apart from
human direction as it senses other objects in the immediate environment or monitors the
human driver’s behavior or biometrics. These technological tools, which reduce the
opportunity for human error, augment human driving capacity – and as ‘humans and
machines work together’ our ‘traditional conceptions of control and responsibility’ may
need to change (Elish, 2019, p.9, p. 22). Despite increasing interest in legal issues arising
from fully or highly automated vehicles, minimal attention has focused on the legal
implications of ADAS in vehicles already currently available.
In jurisdictions where access to compensation for injuries requires proof of fault, this
has significant legal ramifications. In a September 2019 High Court of Australia decision,
DNA on a deployed airbag was critical evidence in determining who was driving the
vehicle at the time of collision: Lee v Lee; Hsu v RACQ Insurance Limited; Lee v RACQ
Insurance Limited [2019] HCA 28 (4 September 2019). Increased safety promised by
ADAS, arguably already evidenced by data, now requires reassessment of risks posed by
‘un-augmented’ human drivers, and whether ‘un-augmented’ driving continues to be
reasonable. Recalibrating risk assessments in this way may act as a policy lever to
encourage more widespread adoption of newer safer vehicle technologies – particularly
as insurers, fleet managers and parties to the chain of responsibility for heavy vehicles
reassess their exposure to negligence claims in light of data about vehicle and driver
performance (Heavy Vehicle National Law (Queensland), 2012). Such recalibration can
also inform legal approaches to risks posed by conditional, high and full levels of vehicle
automation.

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T. LEIMAN

Part I looks backwards briefly to consider how tort law has responded to technological
change in the past. Part II explains the widely used SAE taxonomy for automated vehicles
(Levels 0–5 vehicles, with automation ranging from none, driver assistance, or partial, to
conditional, high or full) before delving more deeply into advanced driver assistance
systems [ADAS] and driver-monitoring devices currently available. Part III outlines the
Australian fault-based liability system and in particular how the elements of reason­
ableness and foreseeability operate in the context of driving. Part IV explores challenges
to existing conceptions of reasonableness and foreseeability posed by ADAS, including
those which operate to override a human driver’s capacity to direct the vehicle’s opera­
tion. These issues may inform how the law responds to level 3, 4 and 5 vehicles where an
automated driving system monitors the driving environment.

Part I: tort law and technological change
The common law has always formulated, modified and changed legal rules to achieve
public policy goals, (Kaczorowski, 1990, p. 1199) ‘constantly adapt[ing] to technological
change’ (Bennett Moses, 2003, p. 395). Tort law has been underpinned by a ‘fundamental
moral principle’ that one who violated community standards of reasonable behavior and
injured another was morally and therefore legally bound to compensate the victim’
(Kaczorowski, 1990, p. 1128). Historically it has been used ‘to make people behave in
morally appropriate ways by holding them to community standards of reasonable
behavior in the circumstances in order to minimize injuries and losses, and to promote
honesty and fairness in economic relationships’ (Kaczorowski, 1990, p. 1128).
Tort law is no stranger to the challenge of apportioning liability for harms caused by
emerging technologies. ‘Technological change occurs against a backdrop of social,
cultural, and economic forces that in turn shape the trajectory of the technology itself’
(Calo, 2019, p. 90). The tort of negligence developed and expanded ‘in the wake of the
Industrial revolution’ (Crootof, 2019, p. 8) and rising rates of injuries caused by emerging
technologies such as railroads, mechanized factories, horseless carriages, and mass
produced consumer products. Some argue it responded to protect those driving such
progress (e.g. railroad companies, and large scale manufacturers) by limiting their
liability (Crootof, 2019, p. 8). Tort law has thus shaped and been shaped by ‘the risks
created by technological innovation but also by the alternative compensatory and
regulatory “technologies” that were introduced to control those risks or mitigate their
effects’ (Oliphant, 2014, p. 821). Nevertheless, ‘it takes time for any innovation to become
fully assimilated within everyday tort law’, and ‘the precise timetable for this process, or
its final results’ is impossible to accurately anticipate (Graham, 2012, p. 1242). This leads
some to argue ‘that traditional tort theory is inadequate to address the expanding scope of
risks in the post-industrial world’ and that risks ‘finding their way into litigation are more
complex and less intuitive to establish’ (Guzelian, 2005, p.1034). Responses to these
concerns have led to introduction of compulsory third party insurance and more recently
of no-fault motor accident compensation systems (Fronsko & Woodrooffe, 2017), espe­
cially for catastrophic injuries (Australian Government The Treasury, n.d.). They form
only part of an increasingly complex regulatory framework aimed at ensuring safety: road
design guides (Austroads, 2017); standards for road traffic, signs and signals (Standards
Australia, n.d.; United Nations,; United Nations, 1968); national standards for vehicle

POLICY AND SOCIETY

253

safety (Motor Vehicles Standards Act (1989); Australian Light Vehicle Standards Rules
(2015); Road Traffic (Light Vehicle Mass and Loading Requirements) Regulations, 2013;
Road Traffic (Light Vehicle Standards) Variation Rules, 2016 (SA); Road Traffic (Light
Vehicle Mass and Loading Requirements) (Light Vehicle Standards Rules) Variation
Regulations, 2018 (SA); Road Traffic (Light Vehicle Standards) Rules, 2018 (SA)); driver
training and licensing (Motor Vehicles Act, 1959 (SA)); road rules which prescribe driver
behavior and use of safety devices such as seatbelts, helmets, vehicular lights and warning
devices (Australian Road Rules as adopted by the Road Traffic Act, 1961(SA); criminal
offences relating to driving or vehicle use (Criminal Law Consolidation Act, 1935 (SA) s,
19A, 19AB, s.19AC, s.19AD; Road Traffic Act (1961))); separate regulation for heavy
vehicles (National Transport Commission, n.d.); vehicle roadworthiness (Road Safety
(Vehicles) Regulations, 2009 (Vic)); repairer licensing (Motor Vehicle Repairers Act,
2003 (WA)); and second hand motor vehicles sales (Second Hand Vehicle Dealers Act,
1995 (SA)).
Where crashes have involved vehicles in completely autonomous mode, the tendency
still seems to be to blame the human involved, even though there may be plausible
reasons for characterizing the technology as at fault (Crootof, 2019, p47; Elish, 2019;
Graham, 2012, pp.1260–1266; Calo, 2016b). Guzelian (2005, p. 990) claims tort law ‘does
not sufficiently accommodate the expanding scope of contemporary risks and the accel­
erating pace of risk assessment and risk discovery,’ and that ‘society can only arbitrarily
delineate which risks to address and which to ignore’ (2005, p. 1012). Crootof (2019,
p. 69–70) suggests ‘[o]ur choices now will determine whether law evolves to preserve or
constrain industry’s new, tech-enabled powers.’ The more humans interact with auto­
mated vehicles, and augment our capability with technological tools such as ADAS, the
more knowledge and experience we will have in predicting their behavior. This may allow
for more accurate risk prediction (Guzeklian, 2005, p.1034; Karnow, 2013, p. 18), and
more effective risk management (Lyndon, 1995, pp.141–142) – not only of risks posed by
machines but of risks posed by humans interfacing with them. ‘[S]ocial norms and
expectations’ have a role in integrating emerging technologies into existing legal frame­
works and then in ‘legal interpretations’ of that technology (Elish, 2019, p. 17–18).
Crootof (2019, p. 51) describes this as an interactive process:
‘Just as technological development can spur legal evolution, legal defaults and tech-enabled
capabilities influence social norms and expectations . . . Once social norms are established,
they affect how legal questions are evaluated.’

The tort of negligence embeds notions of reasonableness and foreseeability at progres­
sively narrower levels of specificity at various stages of legal assessment (Minister
Administering the Environmental Planning and Assessment Act 1979 v San Sebastian
Pty Ltd, 1983; Osborne Park Commercial Pty Ltd v Miloradovic, 2019). Assessment of
and response to risk are thus key elements. But ‘truly novel affordances tend to invite
reexamination of how we live’ (Calo, 2019, p. 90). When automated systems control how
a vehicle or its driver respond to the surrounding environment or to other road users, or
when detailed data about individual vehicle performance and driver behavior is available
in real time, this may force reframing of assessments about what can and should be
foreseen, by whom, when, and what precautions a reasonable person would and should
take in response to foreseeable risk.

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T. LEIMAN

It is important to acknowledge that negligence is only one tool for apportioning liability
for harm caused by motor vehicles. In those jurisdictions with no-fault compensation
schemes for motor accident personal injuries (Brady, Burns, Leiman, & Tranter, 2017),
proving third party fault remains a key determinant for recovery of property damage, with
third party property insurance optional. In Australia, while ‘safety of transport activities
relating to a heavy vehicle is the shared responsibility of each party in the chain of
responsibility for the vehicle’ (Heavy Vehicle National Law (South Australia) Act 2013,
s.26A (1)), assessment of whether that statutory safety duty is met involves considerations
of ‘public risk’ and what is a ‘reasonably practicable’ response (Heavy Vehicle National Law
(South Australia) Act 2013 (SA) Heavy Vehicle National Law Schedule 26A(1), 26C). ‘Due
diligence’ includes ‘acquir[ing], and keep[ing] up to date, knowledge about the safe conduct
of transport activities’ and ‘gain[ing] an understanding of . . . the hazards and risks,
including the public risk, associated with [the legal entity’s transport] activities’ (s.26D).
Assessment of risk and reasonable practicability thus remain relevant, even if not required
to prove fault in negligence. A similar general safety duty on an automated driving system
entity is currently under consideration by Australia’s National Transport Commission.
(National Transport Commission, 2019). By contrast, the UK’s (Automated and Electric
Vehicles Act 2018) (s.2) makes insurers liable for death, personal injury and property
damage ‘caused by an automated vehicle when driving itself on a road or other public
place in Great Britain’ without the necessity for considering fault or reasonableness.

Part II: vehicle automation and ADAS
Legal issues raised by ADAS occur within a broader context of vehicle automation. The
Society of Automotive Engineers (SAE) International Standard J3016, widely used inter­
nationally as a taxonomy for automated vehicles, is ‘descriptive and not intended to be
prescriptive [and is] technical rather than legal’ (Eliot, 2017).
In SAE Level 3, 4 and 5 vehicles, an automated driving system (as opposed to a human
driver) monitors the driving environment. Although often referred to as driverless or
autonomous, (Calo, 2016b, p.215, 227) prefers the term ‘emergent’,
‘because autonomy . . . connotes an intent to act that is actually absent in robots. Emergent
behavior refers to the ability or tendency of a system to behave in complex, unanticipated
ways . . . the idea is that the system will solve a problem (or create one) in ways the
programmers never envisioned.’

Full automation (Level 5) is defined as ‘full time performance by an automated driving
system of all aspects of the dynamic driving task under all roadway and environmental
conditions that can be managed by a human driver’ (Society of Automotive Engineers,
2014). High automation (Level 4) is defined as ‘the driving mode-specific performance
by an automated driving system of all aspects of the dynamic driving task even if
a human driver does not respond appropriately to a request to intervene’ (Society of
Automotive Engineers, 2014). Conditional automation (Level 3) is defined as ‘the
driving mode-specific performance by an automated driving system of all aspects of
the dynamic driving task with the expectation that the human driver will respond
appropriately to a request to intervene’ (Society of Automotive Engineers, 2014). Risks
posed by humans handing over and taking back control of Level 3 vehicles might

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suggest a move directly to Level 4 and particularly Level 5 vehicle is more likely, rather
than incrementally moving through Level 3 (Eliot, 2017). Some have even proposed
that ‘on failure of the self-driving function in the vehicle, the system could return
control to a remote human driver located in response centers distributed across the
world’ (Lei Kang, Zhao, Qi, & Banerjee, 2018).
This contrasts with SAE Levels 0, 1 and 2, where the human driver monitors the
driving environment. Level 2 (partial automation) envisages ‘specific execution by one or
more driver assistance systems of both steering and acceleration/deceleration using
information about the driving environment and with the expectation that the human
driver perform all remaining aspects of the dynamic driving task’ (Society of Automotive
Engineers, 2014). The boundary between Level 3 and Level 2 is blurry – ‘autonomy is
a matter of degree’ (Karnow, 2013, p. 4). It may depend on whether the driver can and
should rely on a Level 2 automated system (that in ‘some instances . . . operates exclu­
sively subject to driver-monitoring’ e.g. ‘normal highway driving cruise control’) or
whether the driver should take back control of a Level 3 system ‘that monitors the
roadway “under some circumstances”’ to negotiate ‘narrow mountainous terrain’ or in
‘exceptionally hazardous weather conditions’ (Abraham & Rabin, 2019, p. 140).
Level 1 describes vehicles with driver assistance, when the driving mode is ‘specific
execution by a driver assistance system of either steering or acceleration/deceleration
using information about the driving environment and with the expectation that the
human driver perform all remaining aspects of the dynamic driving task’ (Society of
Automotive Engineers, 2014).
‘In the Level 1 and Level 2 stages, these systems can briefly take active control of the car to
assist in parking, prevent backing over unseen objects and avoid collisions by braking or
swerving. Sometimes the system actively controls an individual feature of the automobile,
such as adapting front headlights automatically to upcoming curves and other changing
conditions’ (Sagar, 2017, p. 3)

Level 0 vehicles have no automation with ‘the full time performance by the human driver
of all aspects of the dynamic driving task, even when enhanced by warning or interven­
tion systems’ (Society of Automotive Engineers, 2014).
Current vehicles with increasingly sophisticated ADAS safety features might be
described as Level 1 driver assistance or even Level 2 partial automation (Society of
Automotive Engineers, 2014).
ADAS include:
● Crash Avoidance Safety Features: Electronic Stability Control (ESC)1 *; Auto

Emergency Braking (AEB) (Higher speed, Lower speed, Pedestrian); Traction
Control; Intelligent Speed Assist; Active Braking Systems
● Car Safety Features: Driver attention detection (monitoring both eye-gaze and
emotion (e.g. SmartEye, n.d.)); Antilock Braking System (ABS); Reversing camera;
Forward Collision warning; Active Cruise Control; Brake Assist System; Blindspot
1

* ‘Electronic Stability Control (ESC) helps drivers to avoid crashes by reducing the danger of skidding, or losing control as
a result of over-steering. ESC becomes active when a driver loses control of their car. It uses computer controlled
technology to apply individual brakes and help bring the car safely back on track, without the danger of fish-tailing.’
http://www.howsafeisyourcar.com.au/Electronic-Stability-Control/

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T. LEIMAN

Warning System; Lane departure Warning; Lane Keep Assist; Precrash Safety
System.
Other safety features currently available include vehicle telematics and tracking systems;
alcohol/drug ignition interlock devices (e.g. VicRoads, n.d.; Kaufman & Wiebe, 2016,
pp.865–871), programmable smart keys, tyre pressure monitoring, electronic data recor­
ders (onboard diagnostic devices), trailer stability control, and self-parking (e.g. BuyaCar,
2020). Various proprietary aftermarket systems using GPS can track vehicle location and
operation in real time (including driver behavior and style) (e.g. Vipertrak, 2019; de
Vries, de Koster, Rijsdijk, & Roy, 2017; SmartEye, n.d.) and assist with driver fatigue
management. Driver-monitoring ‘systems may use biometric technology . . . to identify
the characteristics of individual drivers and create a history of their driving performance
in order to measure short and long-term fluctuations in drivers’ performance’ (Tsapi,
2015 citing Turetschek, 2006). Wearable technology attached to the driver’s body (not the
vehicle) can increase safety too: smart headsets can ‘[capture] fatigue and distraction in
real time and pre-alerts drivers at the first signs of risk’ (e.g. Maven Machines, n.d.);
smartwatches monitor biometrics in real such as heart rate, stress and drowsiness (e.g.
Cassey, 2016; Fujitsu, 2015; Garmin, 2019; Russey, 2018). External technologies, such as
vehicle activated and intelligent signs, are also designed to increase safety and prevent
crashes (e.g. Westcotec, n.d.). UK Police are piloting mobile phone detection products to
identify drivers using their phones (BBC News, 2019; Westcotec, n.d.; and e.g. L&G
International, 2019).
Although not Level 3, 4 or 5 vehicles, these ADAS features may effectively override
human drivers’ capacity to direct vehicle operation. Some prevent drivers from operating
the vehicle at all, some become active when drivers lose control, others alert drivers to
imminent risks, and yet others shut down the vehicle or other devices when being
operated unsafely.
This demonstrates just how far vehicle safety has come. Seatbelts were one of the first
safety technologies fitted to motor vehicles (Defensive Driving, 2016). Australia led the
world in introducing ‘legislation for compulsory wearing of seat belts’, in 1970 – first in
Victoria, then followed within 14 months by the other Australian states (McDermott &
Hough, 1979). Even though ‘surveys of usage show that the public lagged behind for
decades before wearing became almost universal’ (BITRE, 2010, p. 3) introduction
resulted in a ‘dramatic fall in fatalities and in the number and severity of injuries’, and
claims that seatbelts would lead to an increase in risk taking behavior were not borne out
by empirical evidence (Luntz, Hambly, Burns, Dietrich, & Foster, 2013, pp.344–345;
BITRE, 2010, p.3; Centers for Disease Control and Prevention, n.d.). More recently,
airbags and ESC together are estimated to have reduced the fatality rate per kilometre
travelled by 23%, relative to a base case without these technologies (BITRE, 2015). The
effectiveness of ESC in ‘reducing single-vehicle crashes, particularly run-off-road crashes’
(BITRE, 2015, p. 2), is particularly important given that in 2015–2016 44% of all crashes
on Australian regional and remote roads were single vehicle run-off crashes; the road
death rate per 100 000 population on regional and remote roads (11.8) was almost five
times that in major cities (2.5); and 66% of road deaths occurring in regional and remote
areas (BITRE, 2018). These statistics raise questions about whether risks of motor vehicle
injury should be calibrated differently depending on whether the vehicle was fitted with

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257

ESC. As data generated by ESC and other ADAS features grows, and evidence of safety
impact builds, this will become more pressing for governments, insurers and others
seeking to reduce levels of road trauma.
ADAS features tend to ‘appear initially in high-end models, then migrate to midrange
vehicles and eventually become available on all new cars (Estl, 2015, p. 3). Price points for
high-end models place them out of reach for many, especially younger or elderly drivers,
who may be statistically most at risk of involvement in a crash. ‘[W]here safety is
concerned, insurance companies, regulatory bodies and legislatures normally become
involved, accelerating phase-ins through favorable premiums and legal mandates’ (Estl,
2015, p. 3). Although yet to occur in any real way in response to ADAS in Australia, some
Australian insurers have now indicated they will no longer insure ‘any vehicle with less
than a four-star Australasian New Car Assessment Program crash rating’ (ANCAP
Safety, 2012; WhichCar staff, 2017).
In Australia, the average age of all vehicles is 10.1 years, with passenger vehicles
slightly younger, both averages significantly older than those in similar countries
(Potterton & Ockwell, 2017, p. 6). Vehicles significantly older than average may have
little more than seatbelts. Others will have only low end ‘Crash Protection Features’ such
as crumple zones2 *; strong occupant compartment; impact protection; airbags; seat belts;
and head rests (TAC, n.d.). Although vehicles with more sophisticated ADAS will
increase over time with fleet regeneration, because of price, few may be fitted with the
full complement of ADAS available at any time, and even then will be continually
superseded by new models, in many cases before optimum replacement cycles.
Operating ADAS successfully to increase vehicle safety will ‘[depend] largely on the
users’ ability to correctly work with the systems, [and] be aware of their potential and
limitations in order to take full advantage of them’ (Tsapi, 2015, p.vi). But Australian
learner drivers do not require training in how to most effectively ‘interact with these
technological innovations’ (Tsapi, 2015, p.vi; Regan, Prabhakharan, Wallace,
Cunningham, & Bennett, 2020). Apart from medical assessments for senior licence
holders (NSW Centre for Road Safety, 2015), unrestricted licence holders have limited
or no requirements to regularly update driving skills to include correct operation of new
safety features (Austroads, 2020). Any training received is likely to be at point of purchase
from a salesperson, rather than from a professional driving instructor (Regan et al., 2020,
p. 72–73). Training for professional driving instructors in Australia does not explicitly
refer to competency in operating ADAS (Australian Government, n.d.a). ADAS features
differ across vehicles, magnifying risks due to lack of familiarity. If drivers do not
understand how systems work (Abraham, Reimer, & Mehler, 2017), or do not trust
results they produce (e.g. ‘false alerts from less reliable systems’ (Kidd et al., 2017), they
cannot or will not use them appropriately – resulting in over-reliance, under-reliance,
expecting ‘a system to work outside of its operational design domain’ (Abraham et al.,
2017, p. 1954), or choosing not to use them at all (Kidd et al., 2017, p.S44).
This is further complicated as Australia no longer has a domestic auto-manufacturing
industry. All vehicles and ADAS made after October 2017 (Ladd, 2017) will have been
2

* ‘[C]rumple zones are areas of a vehicle that are designed to deform and crumple in a collision. This absorbs some of the
energy of the impact, preventing it from being transmitted to the occupants’. Ed Grabianowski, ‘How Crumple Zones
Work’. how stuff works https://auto.howstuffworks.com/car-driving-safety/safety-regulatory-devices/crumple-zone.htm

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T. LEIMAN

designed and manufactured elsewhere, with systems optimised for different traffic con­
ditions. However, ‘[d]river behavior differs from one culture to another,’ and ‘situations
that might be considered dangerous’ in one country ‘seen as quite typical’ to drivers in
another, even when ‘traffic rules and regulations are similar’ (Lindgren et al., 2008). Not
accounting for such differences in both design and training may be ‘potentially danger­
ous’ (Lindgren et al., 2008) – possibly unanticipated by either system designers in one
country or drivers in another. Studies highlight that this can result from statistical bias,
where the data used to train the vehicle is ‘not statistically representative’ of the popula­
tion in which it is deployed, which could lead to the vehicle learning ‘localized patterns’
that do not apply in other contexts (Lim & Taeihagh, 2019). For example, where forward
collision warnings optimized for one market sound continually in another, when drivers
in the latter would consider the situation ‘normal or safe’, those warning systems become
increasingly ineffective, annoying and likely to be shut off (Lindgren et al., 2008). As
Radu, 2020 has identified, the ‘centrality of the national state’ and ‘concepts such as
territory’ remain ‘deeply embedded’ in discourse regarding governance of AI, although as
evidenced here, policies and design choices optimising performance of autonomous
systems for one governance environment can play out very differently when applied
elsewhere.

Part III: fault-based liability, reasonableness and foreseeability
Australian ‘[r]oad accident victims are far more likely to make claims and receive tort
compensation than any other group’, with compensation managed through a patchwork
of state and territory legislation. Some jurisdictions allow access to compensation on
a no-fault basis (National Transport Commission, 2018, p.8). This Part however focuses
on fault-based jurisdictions, where claims for compensation are founded in negligence
(National Transport Commission, 2018, p.99). In these jurisdictions, almost half of those
injured are not compensated, with ‘large claims . . . more likely to be rejected or to lead to
an allegation of contributory negligence’, reducing awards of damages (Luntz et al.,
2013, p. 10).
The common law duty to take reasonable care owed by one road user to another has
been clearly recognized in Australian law (Cook v Cook, 1986; Imbree v McNeilly, 2008).
It arises because it is reasonably foreseeable that the actions of one road user could cause
harm to a determinable class, namely other road users. Notions of reasonableness and
foreseeability are essential. Foreseeability is assessed prospectively, without the benefits of
hindsight (Roads and Traffic Authority of NSW v Dederer, 2007), at ‘the duty, breach and
remoteness [scope of liability] stages . . . which progressively decline from the general to
the particular’ (Minister Administering the Environmental Planning and Assessment Act
1979(1983) v San Sebastian Pty Ltd, 1983; Osborne Park Commercial Pty Ltd -VMiloradovic, 2019). The test of foreseeability is ‘undemanding’ (Shirt v Wyong Shire
Council, 1978, 542): ‘any risk, however remote or even extremely unlikely its realisation
may be, that is not far-fetched or fanciful, is foreseeable’, although ‘the line between a risk
that is remote or extremely unlikely to be realised, and one that is far-fetched or fanciful
is a very difficult one to draw’ (Koehler v Cerebos (Australia) Ltd, 2005, 57).
The standard of care required is that of a reasonable person in the driver’s position in
possession of all information the driver either had, or ought reasonably to have had, at

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the time of the incident out of which the harm arose (Civil Liability Act, 1936, s32). This
reasonable person* is ‘of ordinary intelligence and experience . . . independent of the
idiosyncrasies of the particular person whose conduct is in question’,[ii] not ‘unduly
timorous’ nor ‘nonchalantly disregarding obvious dangers’, ‘free both from overapprehension and from over confidence’ (Glasgow Corp v Muir, 1943). The standard is
not lowered for inexperienced or unqualified drivers (Cook v Cook, 1986; Imbree v
McNeilly, 2008). Courts require motorists to drive defensively, alert to potential dangers
(Luntz et al, 2018, p.351), and drivers cannot rely on the safe driving of others (Sibley v
Kais, 1967). This standard of care focuses on what can reasonably be expected of the
human driver and their act of driving, not on the performance capabilities of the vehicle
they were driving or the technology they were using. As Gahnberg (2020) has noted, the
presence of formal rules (road rules) and informal norms (community expectations as to
safe driving) ‘carry the meaning of what is good or acceptable behavior’ in [this] context’.
Once the standard has been established, legislative tests must be applied to determine
whether it has been breached (e.g Civil Liability Act 1936, s.24). The test for breach,
largely similar across Australian jurisdictions, codifies an earlier common law test
(Wyong Shire Council v Shirt, 1980) and explicitly incorporates both reasonableness
and foreseeability.
32 – Precautions against risk
(1) A person is not negligent in failing to take precautions against a risk of harm unless –
(a) the risk was foreseeable (that is, it is a risk of which the person knew or ought
to have known); and
(b) the risk was not insignificant; and
(c) in the circumstances, a reasonable person in the person’s position would have
taken those precautions.
(2) In determining whether a reasonable person would have taken precautions against
a risk of harm, the court is to consider the following (amongst other relevant things):
(a) the probability that the harm would occur if precautions were not taken;
(b) the likely seriousness of the harm;
(c) the burden of taking precautions to avoid the risk of harm;
(d) the social utility of the activity that creates the risk of harm. (Civil Liability
Act, 1936 (SA))
A driver will be negligent if they do not meet the standard of care required. Examples
could include failing to keep a proper lookout, failing to comply with road rules or
signage, being distracted while using a mobile device, or failing to exercise sufficient
control of their vehicle.
Proving causation is a two-step process requiring consideration of both factual
causation, and scope of liability. Tests for factual causation do not involve either reason­
ableness or foreseeability. Where it is alleged that harm has been caused by the absence of
appropriate traffic warnings or road signage, the plaintiff must prove that the driver
would have complied with any speed limits or other traffic directions indicated if signs
were present (Roads and Traffic Authority v Royal, 2008; Commissioner of Main Roads v
*

who is apparently not gendered, although Bender, Finlay and other scholars suggest that the substitution of ‘person’ for
‘man’ has ‘concealed other ‘masculine’ values inherent in the concept (Luntz et al, 2013, p.212).

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T. LEIMAN

Jones, 2005; ; Roads and Traffic Authority of NSW v Dederer, 2007; March v E & MH
Stramare Pty Ltd, 1991). Determination of whether responsibility for the harm is within
the scope of defendant’s liability (See for example Civil Liability Act 1936 (SA) s.34(1)(b)
and (3)) draws on common law tests for remoteness (i.e. whether the kind of damage
suffered was foreseeable as a possible outcome of the kind of carelessness alleged)
(Overseas Tankship (UK) Ltd v Morts Dock & Engineering Co Ltd (The Wagon Mound
(No 1), 1961).
Once liability is proved, an injured person may face an allegation that they failed to
‘exercise reasonable care and skill for their own protection’, and thus were contributorily
negligent (Civil Liability Act 1936 (SA) s.3) If this can be proved by the defendant on the
balance of probabilities, damages may be reduced on the basis of a ‘just and equitable
apportionment’ reflecting comparative culpability (See e.g. Law Reform (Contributory
Negligence and Apportionment of Liability) Act 2011 (SA) s.7; Pennington v
Norris (1965)). Failure to wear a seat belt has since 1976 been generally regarded as
contributory negligence, resulting in reduction of damages (Froom v Butcher, 1976) and
more recently will give rise to a rebuttable presumption of contributory negligence with
fixed reductions (see e.g. Civil Liability Act 1936 (SA) s.49; Motor Accident Injuries Act
2017 (NSW) s 4.17; Civil Law (Wrongs) Act 2002 (ACT) s.97). Failure to wear a safety
helmet as required, or travelling whilst not in the passenger compartments give rise to
similar presumptions (see e.g. Civil Liability Act 1936 (SA) s. 49). Driving while
intoxicated, or relying on the care and skill of a driver known to be intoxicated also
give rise to statutory presumptions of contributory negligence resulting in a sliding scale
of reductions (see e.g. Civil Liability Act 1936 (SA) s. 46 and 47).

Part IV: ADAS – new questions about foreseeability and reasonableness
Even small reductions in human error when operating motor vehicles will save signifi­
cant lives, prevent injuries and property losses. In 2018, road traffic was ‘the eighth
leading cause of death globally . . . [claiming] more than 1.35 million lives each year and
[causing] up to 50 million injuries’ (World Health Organization, 2018). Australia esti­
mates the national annual economic cost of road crashes at $AU27 billion per annum,
with 1226 deaths in 2017. At least 90% of traffic collisions are caused by human error,
with the vast majority rear end crashes. Yet large numbers of drivers still admit to
undertaking unsafe behaviors when driving – including using mobile devices, driving
while fatigued, and falling asleep at the wheel.
Should humans only be allowed to drive a motor vehicle if their capacity to do so is
‘augmented’ appropriately by technology? Should users of vehicles that are not so
equipped be regarded as putting themselves and the community at unacceptable levels
of risk? Should drivers be required to wear devices that monitor behavior or biometrics?
Although of little import in jurisdictions where access to compensation does not depend
on proving that another driver was at fault, in fault-based jurisdictions, where negligence
must be established before compensation can be recovered, these questions go to both
foreseeability and reasonableness and so will be critical. Assessing appropriate responses
to risk in this era of augmented human driving capacity poses new challenges. This is ‘the
puzzle of how to deal with the contingency of technology and its social impacts’ (Calo,
2019, p. 88), even for Level 0,1 and 2 vehicles without greater levels of automation or

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261

autonomy. Those challenges will intensify for Level 3 and 4 vehicles, if human drivers can
hand over and resume control.
The data outlined above raise new questions about whether it continues to be reason­
able to operate vehicles on Australian roads without ADAS (at a minimum, without
airbags and ESC) or without driver-monitoring technology. Tort law’s response to
seatbelts might suggest that even where safety features have not yet been mandated
legislatively, contributory negligence might still be found where drivers failed to use
features that were available. Data from vehicle telematics and driver – monitoring now
allows for granular assessment of specific risks – i.e. risks posed by an individual driver
with a particular driving history operating a vehicle with specific features in a particular
locale or under particular conditions both internal to the driver and external to the
vehicle. This data might be accessible to the driver, owners, fleet managers, insurers or
others in real time. Usage-based insurance is already pricing data-driven risks differently
(Allied Market Research, 2016; Smith, 2019; Tselentis, Yannis, & Vlahogianni, 2017), and
is predicted to develop significantly in future, moving towards a ‘predict and prevent
methodology’ (Balasubramanian, Libarikian, & McElhaney, 2018, p. 18). Vehicle tele­
matics tools are widely used by fleet managers to manage costs and productivity and
boost safety. Approved security camera systems are mandated in Australian taxis (see e.g.
Department of Transport and Main Roads, n.d.), and in many other international
jurisdictions (Topham, 2019). At a more personal level, many people already wear
personal fitness trackers, smartwatches heart rate sensors and carry smartphones with
GPS location tracking. In a world where behavior is already impacted by the data
generated by such devices, it may not take much for the community to accept the need
for ‘augmented driving’, or conclude that failing to increase safety by augmenting human
capacity with the use of available technology breaches the duty of care owed to other road
users.
This makes articulating the standard of care now required of a reasonable driver
difficult. Where fault must be proved, linking liability to foreseeability imposes a key
limiting principle, derived from the necessity of assessing the morality of an action
(Hardie, 1992), – only holding ‘defendants accountable if they did know or should
have known that they could cause harm’ (Calo, 2016b, p. 231). As sophisticated ADAS
increasingly change the level and nature of risk posed by vehicles, the community’s
perception of risk or the standard of care required may no longer be accurately informed.
Lack of knowledge about, and therefore lack of capacity to foresee the extent to which
ADAS reduce risks posed by common driver behavior (e.g. distraction, speed, position
on the roadway, etc.) may mean the risk posed by a human driver in an older car is rated
no higher (by courts or by drivers themselves) than a human driver in a car with all of the
safety features currently available, even though data may suggest a very different assess­
ment should be made. Existing legal tests construct the reasonable driver as a person in
the defendant’s position with all the information the defendant either has or ought
reasonably to have had (Civil Liability Act 1936 (SA), s.31). Most drivers and passengers
will have very little knowledge about how ADAS work. ADAS are proprietary systems
with significant commercial value to their designers and manufacturers, so the public
may have very little access to detailed information about how those systems operate in
any event (Selbst, 2020, p. 50). Gahnberg (2020) describes this as ‘artificial agency’ –
ADAS effectively operate as ‘decision-makers’ to regulate driving behavior in ways that

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may be ‘unpredictable and opaque’ to human road users. The following discussion
considers possible alternative approaches.
Firstly, the standard of care could take account of the age and capabilities of the
vehicle. This begs the question: Should drivers of newer safer cars therefore be expected
to know how to operate its ADAS most effectively, even if that required undertaking extra
training? Given that such training is not yet required to obtain a driver’s licence, or even
to qualify as a professional driving instructor, this may not be considered reasonable by
current community standards, and therefore would be unlikely to be adopted by the
courts as a legal standard of care. It could also mean that a reasonable driver in a vehicle
with none of those features might not be expected to have undergone such training or to
have considered the use of wearable monitoring devices, even though such wearables
would still significantly improve safety. Perversely, this would impose higher standards
on drivers choosing safer new technology, while providing no incentive to mitigate the
risks of using older technology which posed great risks of harm to the community,
advancing neither interests either of injured road users or the broader community who
inevitably also share the costs of road trauma.
As more vehicles have more automated or autonomous elements, perceptions will
change: ‘When the reallocation of a function from human to machine is complete and
permanent, then the function will tend to be seen simply as a machine operation, not as
automation’ (Parasuraman & Riley, 1997, p. 231). This creates a conundrum. Vehicles
with ADAS are not the same as vehicles without – and perhaps could be described as
a partially ‘qualitatively distinct affordance’ (Calo, 2019, p. 86). If, however, ADAS are
seen simply as vehicle ‘operation’, despite lack of widespread understanding about their
capacity to override or augment human driving, this qualitative difference is ignored,
effectively simply equating ADAS with earlier safety technologies in applications of legal
tests.
A second approach to setting the standard of care might focus primarily on the
reasonably experienced qualified human driver in the position of driving in the circum­
stances external to the vehicle (i.e. at night, in heavy traffic, in wet weather) – rather than
focussing on the vehicle’s level of automation or ADAS. This second approach poses its
own challenges. What should be required when humans interface with vehicle safety
systems optimised for different traffic conditions? Most drivers will have little under­
standing of how ADAS systems have been optimised and for what conditions. This lack
of knowledge has already been identified as potentially dangerous – i.e. ‘use, misuse,
disuse, and abuse of automation’ (Parasuraman & Riley, 1997, p. 233). If a reasonable
driver is required to have this information, this will significantly impact on driver
training and upskilling and appropriate induction for use of different vehicles.
Increasing awareness of AI and knowledge regarding its use has therefore been empha­
sised in recent AI policy documents as a key responsibility of governments to maximise
AI’s social benefits and minimise its misuse and risks (Ulnicane 2020).
Assuming these difficulties can be overcome, and an appropriate standard of care can
be identified that aligns with community expectations, the next step is to consider
whether that standard has been breached. Assessing whether the standard of care has
been met requires consideration of whether a reasonable person facing a foreseeable risk
would have taken ‘any action to avoid or reduce the risk of harm’ against that risk of
harm.

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Data shows it is foreseeable that human error is likely to cause motor vehicle crashes
and that ADAS can significantly reduce both likelihood of collision and the capacity for
that error to adversely impact vehicle operation. Given this foreseeable risk, what
precautions should a reasonable person take in response? Is it unreasonable to continue
to use vehicles without ADAS either as a driver or passenger, or not to enhance safety in
older vehicles with driver-monitoring technology unreasonable? If this approach is
adopted, then presumably drivers and passengers could not be expected to use only
vehicles with the highest available levels of safety, necessitating consideration of what
minimum levels of ADAS or wearables are required. Costs of high-end vehicles might
place this technology out of reach for many, including drivers posing the highest risks.
This may mean the burden of adopting vehicles with ADAS outweighs the probability
and likely seriousness of the risks of harm posed by un-augmented human drivers, and
thus is an unreasonable precaution. Social utility (Civil Liability Act 1936(SA), s.32(2)d))
is also relevant in determining whether precautions should be taken, posing a further
challenge: safer vehicles benefit the whole community, not just individual road users.
A finding of negligence for failure to use a vehicle fitted with commonly available ADAS
would have significant ramifications for the value of existing fleet; the second-hand
motor vehicle industry; compulsory third-party, and first-party and third-party property
insurance. Reducing road trauma would have significant impacts on hospitals and the
health system. Arguably, any move towards this should be a legislative rather than
judicial mandate (Estl, 2015, p. 3). Australian legal responses to seatbelts are instructive
here – statutory reductions for contributory negligence were introduced after the road
rules had made failure to wear a seatbelt an offence, and after common law decisions to
that effect.
Statistics regarding single vehicle run-off crashes make a compelling argument that it
is no longer reasonable to drive ‘un-augmented’ for journeys on regional, rural and
remote Australian roads (i.e. without smart headsets or smartwatches that might alert
distracted or fatigued drivers) – particularly if these tools are available at accessible prices.
Earlier experience of introducing safety technologies might again provide a guide here.
As evidence of safety benefit mounts, choosing vehicles with un-augmented drivers for
these types of journeys could be regarded as contributorily negligent, leading to
a reduction in damages. Such approach could lead to disproportionally adverse con­
sequences for those who live in regional and rural areas, particularly those who cannot
afford to purchase such technologies, and for passengers where no other means of
transport is available (e.g. children, the elderly, and persons with a disability).
However, seat belts are not a direct analogue here – their method of operation is
transparent to all users, they require almost no instruction, no software or system
updates, imposing little obligation on vehicle occupants other than momentary pressure
or discomfort. Requiring human drivers to wear monitoring technologies generates
granular data about personal behavior or intimate health information that might subse­
quently be accessed by employers, fleet managers, insurers, law enforcement, or other
government entities, and so impacts far more broadly on other rights, such as informa­
tional privacy, as well as raises other ethical issues regarding the potential use of ‘vehiclegenerated data’ by government agencies for surveillance of citizens (Lim & Taeihagh,
2018; National Transport Commission, 2020).

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Human drivers, often not able to accurately assess their own performance
(Parasuraman & Riley, 1997, p. 237), may overestimate their own driving competency,
and underestimate the real risks of harm they pose to themselves or others. Vehicle
tracking systems showing patterns of behavior such as harsh braking or accelerating may
identify specific and increased risks. Should drivers review the data generated by their
vehicles about their own behavior? If so, would a driver who chooses not to review data
about their own performance be negligent? Data analysis may require special skills,
adding further complexity – who should be expected to have those skills? Drivermonitoring technology can alert drivers who are distracted or fatigued. Not only is it
foreseeable that human drivers might be distracted or fatigued, these devices potentially
go further – identifying exactly when a particular driver is not paying sufficient attention
to the road ahead, and thus exactly when the risk of collision increases. When should that
data be analysed – in real time, hourly, daily, weekly, annually? If a significantly increased
risk of specific harm is identified in real time, could and should this enliven a legal
obligation to take precautions, and by whom? If such analysis can be performed, reason­
able precautions might range from ensuring a particular driver receives additional
training, or is prevented completely from driving either immediately or in future. This
has potential to extend liability beyond drivers to fleet managers, data analysts or even
those reviewing transport infrastructure data from cooperative intelligent transport
systems (C-ITS), an extension further complicated by the use of algorithms and AI
tools to review large traffic datasets, making the identity of the person liable even less
clear.
There is ‘a social tendency to overestimate the capacity of machines and underestimate
the abilities of humans ’ (Elish, 2019, p. 14) as automated processes are widely perceived
as ‘objective and fair’ (Lim & Taeihagh, 2019, p.5791; Taeihagh, 2020). Ulnicane et al.
(2020) highlights the importance of expectations and hypes surrounding emerging
technologies in shaping the overall governance of the technology. In particular, unrea­
listic expectations of ADAS safety could have significant implications for the standard of
care being applied to determine negligence liability. Regular use of ADAS such as forward
collision warnings, reversing cameras, lane departure warning or blindspot warnings
could have the unintended result of deskilling drivers, and leading them to expect they
can always rely on the vehicle even when they should not do so. Perversely, this could
increase both the risk of human error and the number of crashes. Conversely, the
community may be ‘unwilling to accept from machines what we have come to expect
from humans’ (Coren, 2018, 2016; International Communication Association, 2016),
a view subsequently likely to feed into assessments of breach.
‘One overarching theme in human–automation trust research is that humans generally
expect automation to be “perfect” (i.e., with an error rate of zero), whereas a human is
expected to be imperfect and to make mistakes.’ (Prahl & Van Swol, 2017, p. 693)

Could this mean that unless all ADAS features were shown to be 100% safe 100% of the
time, reliance on those features would be perceived by a reasonable person as an
unacceptable risk. This would mean that any choice to use such a vehicle instead of
a human driver may be an unreasonable response to a foreseeable risk. If so, this would
expose the driver or vehicle user to a finding of negligence. What happens when the
ADAS results in vehicle behavior that is unexpected, or deviates from usual human

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responses or traffic ethics, even if it ultimately produces safer vehicle operation? (com­
pare Selbst, 2020, p.25; Taeihagh, this issue). Unexpected driving behaviors are already an
issue for highly automated vehicles due to the use of unpredictable and probabilistic
algorithms that have already resulted in fatal crashes (Lim & Taeihagh, 2019, p. 19).
Approaches to answering these questions may inform future legal responses to level 3,4
and 5 vehicles too.

Conclusion
Road trauma is incredibly costly – in lives, injuries, and broader losses, with human error
the leading cause. Automated and autonomous vehicles are predicted to bring significant
safety advances – with the dynamic driving task no longer reliant simply on the human
driver. Getting the regulatory frameworks ‘right’ can assist emerging vehicle technology
to be further ‘developed and used in socially beneficial ways and [avoid] potential harms’
(Ulnicane et al., 2020). Level 3 vehicles allow humans to hand over control to an
automated driving system in certain circumstances. Level 4 vehicles perform all aspect
of driving, even where the human does not respond to requests to intervene. Level 5
vehicles remove human drivers altogether. Whether the purported safety benefits of these
vehicles transpire remains to be seen, and governments, regulators and scholars are
wrestling with the implications of these future transport modalities. The bright light of
this novelty may overshadow emerging ADAS and driver-monitoring technologies,
including those already available and in use, especially when they significantly increase
in safety and may have the effect of overriding a human driver’s capacity to direct the
vehicle’s operation. This article has raised questions about the implications of this
‘augmented driving capacity’.
ADAS and driver-monitoring technologies pose challenges for jurisdictions where
access to compensation for road traffic trauma depends on establishing negligence or
where a general safety duty includes consideration of ‘reasonable practicability’.
Reasonableness and foreseeability are central in determining both the standard of care
in negligence and the precautions against risk of harm that should be taken to meet that
standard. Data generated by these technologies already show their use brings substantial
safety gains, thus forcing reassessment of what can be foreseen and what is reasonable to
expect of drivers, passengers, vehicle owners, and others such as insurers, fleet managers
and vehicle data analysts. Critical questions therefore arise about whether it continues to
be reasonable to for ‘un-augmented human drivers’ to operate motor vehicles. While the
experience of earlier safety technologies like seatbelts can be instructive, ADAS and
driver-monitoring technologies are qualitatively different, demanding a different
response. It also brings into sharp relief the even more complex issues in store in relation
to level 3, 4 and 5 automated and autonomous vehicles. Understanding the risks and
benefits of ADAS and driver-monitoring devices presents an opportunity to recalibrate
more accurate community perceptions of driver safety, encourage wider adoption of
safer technologies, and act as a legal and policy lever to create legal frameworks that better
fit ‘the expanding scope of contemporary risks and the accelerating pace of risk assess­
ment and risk discovery’ (Guzelian, 2005, p. 990).

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Acknowledgments
I am very grateful for the very useful feedback I have received in preparing this paper from Dr Araz
Taeihagh, the authors of other articles in this special issue who participated with me in an excellent
workshop in August 2019 hosted by Lee Kuan Yew School of Public Policy at the National
University of Singapore, and finally to the two anonymous reviewers.

Disclosure statement
No potential conflict of interest was reported by the author.

Notes on contributor
Associate Professor Tania Leiman is Dean of Law at Flinders University. Tania has a background
in legal practice, including personal injury law. She has taught Tort law for many years. Tania's
current research interests include future mobility solutions (including automated vehicles and
hyperloop technologies), disruption in the legal profession, artificial intelligence and legal tech,
and the future of legal education. She has received individual and team university teaching
excellence awards, an individual Australian government ALTC citation and a team Australian
government AAUT citation in 2016. She continues to undertake legal consultancy work, particu­
larly in relation to legal implications of grey fleet & light fleet, and regulatory issues re hyperloop
technologies.

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</reference>

<statements>
1. For ADAS-equipped vehicles, most accidents currently fall in supervised autonomy or conventional modes. Scholars like Geistfeld and commentators on UK’s Automated and Electric Vehicles Act note that, for truly automated vehicles driving themselves, insurers or manufacturers may be statutorily liable for death, personal injury, and property damage caused when the vehicle is driving itself. ADAS cases, however, must navigate more complex shared-responsibility boundaries.
2. The UK’s Automated and Electric Vehicles Act already makes insurers liable for harm caused by automated vehicles when driving themselves while preserving recourse against manufacturers.
3. Insurers would have incentives to collect detailed data, identify patterns of defects or misuse, and press manufacturers for safer designs.
</statements>

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