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Version: Accepted Version
Article:
Jamson, AH, Merat, N, Carsten, OMJ et al. (1 more author) (2013) Behavioural changes in
drivers experiencing highly-automated vehicle control in varying traffic conditions.
Transportation Research Part C: Emerging Technologies, 30. 116 - 125. ISSN 0968-090X
https://doi.org/10.1016/j.trc.2013.02.008
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Research Paper
Behavioural changes in drivers experiencing highly-
automated vehicle control in varying traffic conditions
A. Hamish Jamson, Institute for Transport Studies, University of Leeds, UK.
Natasha Merat, Institute for Transport Studies, University of Leeds, UK.
Oliver M.J. Carsten, Institute for Transport Studies, University of Leeds, UK.
Frank C.H. Lai, Institute for Transport Studies, University of Leeds, UK.
Corresponding author:
Hamish Jamson,
Institute for Transport Studies,
University of Leeds,
LS2 9JT.
U.K.
Phone: +44 113 343 5730
Fax: +44 113 343 5334
Email: a.h.jamson@its.leeds.ac.uk
Keywords: driver behaviour, vehicle automation, vehicle control, driving simulator.
*Manuscript
Click here to view linked References
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Abstract
Previous research has indicated the ironies of high levels of vehicle automation
resulting in reduced driver situation awareness, but has also highlighted potential
benefits of such future vehicle designs through enhanced safety and reduced driver
workload. Well designed automation allows drivers’ visual attention to be focused
away from the roadway and toward secondary, in-vehicle tasks. Such tasks may be
pleasant distractions from the monotony of system monitoring. Hence, this study was
undertaken to investigate the impact of voluntary secondary task uptake on the
system supervisory responsibilities of drivers experiencing highly-automated vehicle
control. Independent factors of Automation Level (manual control, highly-automated)
and Traffic Density (light, heavy) were manipulated in a repeated-measures
experimental design. 49 drivers participated using a high-fidelity driving simulator
that allowed drivers to see, hear and, crucially, feel the impact of their automated
vehicle handling. Drivers experiencing automation tended to refrain from behaviours
that required them to temporarily retake manual control, such as overtaking,
accepting the resulting increase in journey time. Automation improved safety
margins in car following, however this was restricted to conditions of light
surrounding traffic. Participants did indeed become more heavily involved with the in-
vehicle entertainment offered than they were in manual driving, affording less visual
attention to the road ahead. This might suggest that drivers appear happy to forgo
their supervisory responsibilities in preference of a more entertaining highly-
automated drive. However, they did demonstrate additional attention to the roadway
in busy traffic, implying that these responsibilities are taken more seriously as the
supervisory demand of vehicle automation increases. These results may dampen
some concerns over driver underload with vehicle automation, assuming vehicle
manufacturers embrace the need for positive system feedback and drivers also fully
appreciate their supervisory obligations in such future vehicle designs.
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1. Introduction
Advanced Driver Assistance Systems (ADAS), which have the potential to
improve both road transport safety and driver comfort, are becoming a major focus in
emerging vehicle designs. There is a recent history of European automobile
manufacturers co-operating on their development of such systems, for example
through the ADASE II (Advanced Driver Assistance Systems in Europe) project
supported through the European Union’s Fifth Framework Programme. This
particular project, involving Peugeot Citroen, Jaguar Land Rover, Fiat and BMW,
culminated with a roadmap outlining how the current manual driving task might be
gradually automated by on-board systems. Follow-on projects, such as HAVEit
(Highly Automated Vehicles for Intelligent Transport) have focussed on improving
sensor technology and system architecture in order to make highly-automated
driving on public roads an achievable ambition over the coming years.
Many ADAS supporting semi-automated vehicle control already exist. A
plethora of manufacturers now offer Adaptive Cruise Control (ACC), which
automatically manages longitudinal control of the vehicle to achieve driver-selected
values for speed and following headway. Amongst other executive models, the BMW
5, 6 and 7 series and the Mercedes Benz S and CL-class all offer full ACC, which is
able to bring the car to a complete stop without any driver intervention. Similarly,
lateral support is commonly provided through Lane Departure Warning, typically
informing the driver of encroachment toward the current lane boundary either by
auditory or haptic warnings. In more extreme cases, such as the Honda Inspire, the
vehicle will actually provide a gentle torque to the steering column to maintain itself
in lane.
As increasing attention is afforded to the development of such systems and
accordingly highly-automated, self-driving vehicles (e.g. Google’s automated Toyota
Prius which, it is claimed
1
, has logged over 140,000 miles around Northern
California), it is inevitable that average motorists will eventually find themselves no
longer actively involved in routine vehicle handling, taking on a purely supervisory
role in ensuring that their vehicle suitably performs the required control actions on
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their behalf. Considering the driver’s ability to undertake such a role is therefore
becoming increasingly vital.
1.1 Human factors of semi and highly-automated driving
The interaction between the human driver and the automated vehicle has been
a focus of applied research for some time (e.g. Nilsson, 1995). During this period,
such technologies were expected to provide significant benefits, highlighted in a
review by Stanton and Marsden (1996) as improved well-being through the reduction
of driver workload and the enhancement of safety through a reduction in error
associated with the inherent restriction on individual driving style. Such studies have
since been refined to include models of driver behaviour in such circumstances
(Boer and Hoedemaeker, 1998; Goodrich and Boer, 2003).
The work presented here characterises driver behaviour with vehicle
automation by way of reference to two well-accepted models. The first, Michon’s
(1985) hierarchical analysis of the driving task, describes driver behaviour at three
distinct levels: the highest strategic level outlining the process of route choice, the
middle tactical level concerning the planning of specific driving manoeuvres to best
achieve the chosen route and the lowest control level depicting the closed-loop
control of vehicle inputs required to action these manoeuvres. The second,
Parasuraman, Sheridan and Wickens’ (2000) task analysis of automation, proposes
four information-processing stages: information acquisition, information analysis,
decision making and action. The highest level of stage 3 automation (decision
making) defines the control undertaken by the highest level of stage 4 automation
(action) without requiring, or even allowing, any human involvement.
Although focused toward a representation of situation awareness, in an
apparent combination of these two task analyses, Ma and Kaber’s (2005) proposed
driver model suggests successful completion of tactical and control level driving
tasks is achieved through high quality information processing, regardless of whether
this information is processed by the vehicle sensors’ (in the case of highly-automated
driving) or by the human operator’s (in the case of manual driving) perception of the
1
New York Times, 9
th
October 2010, “Google Cars Drive Themselves, in Traffic”
