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Details of Grant 

EPSRC Reference: EP/P017517/1
Title: TRANSITION: Transport safety in automated vehicles
Principal Investigator: Wilkie, Professor RM
Other Investigators:
Merat, Professor N Markkula, Professor G Romano, Professor R
Researcher Co-Investigators:
Dr CD Mole
Project Partners:
Department: School of Psychology
Organisation: University of Leeds
Scheme: Standard Research
Starts: 01 September 2017 Ends: 30 November 2021 Value (£): 570,919
EPSRC Research Topic Classifications:
Human-Computer Interactions Robotics & Autonomy
Transport Ops & Management Vision & Senses - ICT appl.
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Dec 2016 EPSRC ICT Prioritisation Panel Dec 2016 Announced
Summary on Grant Application Form
Driver error is a major contributor to many road accidents: there were 194,477 reported road casualties in the UK in 2014 (with an estimated valuation of £16.3 billion; Department for Transport, Reported Road Casualties Great Britain: 2014, Annual Report), and the most commonly recorded factor was the "driver/rider failed to look properly" (DfT, 2014), with four of the five most frequently reported contributory factors involving "driver error or reaction". In this context the increased use of Automated Vehicles (AVs) that can control the vehicle and monitor and respond to road conditions without regular driver input has the potential to dramatically reduce road death. A major concern, however, is that many AVs require human supervision, and despite our lack of understanding how human drivers interact with AVs there are already AV systems that are available for purchase and are being used on the roads (e.g. Tesla). In order to safely implement AV systems we need to understand the capabilities and limitations of drivers re-engaging steering control from AV systems under a variety of conditions.

Project TRANSITION will use sophisticated laboratory-based measures (including advanced vehicle simulators) to examine drivers re-engaging with the vehicle after a period of AV control. We will determine the capability of drivers regaining steering control under conditions that simulate various types of visual and cognitive load (e.g. driving at night, and/or when looking away at a satellite navigation system). These findings will be used to identify situations where drivers are particularly vulnerable to making steering errors, and develop the TRANSITION model of AV-Human transitions that will inform improvements to the design and implementation of AV systems.

This project is critical to improve AV systems to ensure they safely manage AV-human transitions, and to develop more effective human-machine interfaces between drivers and their vehicles. Whilst there has been widespread coverage of the development of fully automated vehicles, it is unlikely that full-automation will quickly become the norm. Indeed 'driverless' vehicles are already technologically possible, but there are significant barriers to adoption, and the prevalent view is that the human driver will remain the primary controller of the vehicle for some time. There are a number of reasons for this, including driving in regions where automation is not possible (e.g. poor GPS coverage, inaccurate mapping or poor road demarcation), needing the human to control the vehicle when automatic systems fail, and not least because some drivers will continue to purchase vehicles that allow them to be in control for some periods. In this context, understanding the best way to ensure safe interactions between human and AVs remains a high priority.

Key Findings
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Potential use in non-academic contexts
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Further Information:  
Organisation Website: http://www.leeds.ac.uk