In March this year the Innovate UK and Centre for Connected and Autonomous Vehicle part funded collaborative R&D project, HumanDrive, came to a close. The HumanDrive consortium consisted of 10 partners, Nissan, Hitachi, Horiba Mira, SBD Automotive, Atkins, Aimsun, Highways England, University of Leeds, Cranfield University and Connected Places Catapult. The HumanDrive project developed an autonomous vehicle, which, in November 2019, completed a 230-mile drive from Cranfield to Sunderland, which was the UK’s longest autonomous journey, to date.
One of the primary outputs from the HumanDrive project was the development of an Advanced Control System (ACS) (by Nissan and Hitachi), that was designed such that the autonomous vehicle would emulate a natural, human-like, driving style. During the course of the project, Human Factors research was carried out by the University of Leeds, Connected Places Catapult and Cranfield University to evaluate the ACS in real-world, and simulator-based trials, to gain insights into the perceived naturalness, comfort and safety of the system under evaluation. These attributes were measured during, and post experiencing, AV driving styles, via think aloud protocols, questionnaires, physiological metrics, and real-time participant feedback. The evaluations also considered how personality traits and personal driving style might influence perceptions of AV driving styles.
This webinar will discuss the project findings, and how these outputs complement a growing body of research exploring ride comfort in the context of AVs, as discussed in the project White Paper: ‘Measuring User’s Comfort in Autonomous Vehicles’ written by Connected Places Catapult’s Ellie Wooldridge, Research & Human Factors Team Lead, and Jamie Chan-Pensley, Principal Technologist. Natasha Merat, will provide an overview of the results from two studies which used the University of Leeds Driving Simulator to investigate how a range of automated driving styles influenced participants’ psychophysiological response, and their real-time subjective level of safety and comfort.