Dengbo He, PhD thesis, Department of Mechanical and Industrial Engineering, 2020
Professor Birsen Donmez, Supervisor
With state-of-the-art driving automation technology available to the public (i.e., SAE Level 2 driving automation), drivers no longer need to control the vehicle continuously, but are still required to monitor the road and the automation, and take over control or adjust the automation’s setting when necessary. Thus, many driving skills, such as anticipatory driving that allows drivers to predict potential traffic changes and respond to them in advance, can still enhance driving safety in automated vehicles.
Anticipatory driving has been found to be more prevalent among experienced drivers in non-automated vehicles. However, the factors influencing anticipatory driving in automated vehicles has not yet been investigated. Thus, this dissertation aims to understand anticipatory driving behaviors in automated vehicles and investigate displays that can support it.
Results show that in both automated and non-automated vehicles, experienced drivers exhibited more anticipatory driving behaviors, and distraction engagement impeded anticipatory driving for both novice and experienced drivers. Further, allocating more visual attention (i.e., glance more) toward cues indicating upcoming events increased the chance of exhibiting anticipatory driving behaviors in non-automated vehicles. For automated vehicles, it was found that drivers’ reliance on automation might have a larger impact on the performance of anticipatory driving compared with visual attention to cues. Providing additional context information (e.g., the speed and the location of the surrounding road agents) supported drivers’ anticipation of potential traffic conflicts.
Access PDF of abstract and full PhD thesis by Dengbo He, “Understanding and Supporting Anticipatory Driving in Automated Vehicles.”
Supervisor contact information
Professor Birsen Donmez
Tel.: (416) 978-7399