Anticipatory Driving

Drivers’ anticipation behaviours in autonomous vehicles

Despite the ambitious plans of vehicle manufacturers, vehicle technologies usually take two to five decades to saturate their potential market, and currently, SAE level-2 automation (SAE J3016_201401) is the state-of-the-art vehicle-automation technology. For this level, drivers are required to visually monitor the driving environment and the automation and anticipate how a given situation may evolve, in order to intervene in a timely manner when necessary. A good design needs to support drivers’ anticipation of both the environment as well as the automated system that they are interacting with. Anticipation in driving in a non-automated context has been defined as “a manifestation of a high-level cognitive competence that describes the identification of stereotypical traffic situations on a tactical level through the perception of characteristic cues, and thereby allows for the efficient positioning of a vehicle for probable, upcoming changes in traffic.” (Stahl, Donmez, & Jamieson, 2014, p. 603). Anticipatory driving has been shown to be more prevalent among experienced drivers (Stahl et al., 2014), but to be inhibited by engagement in secondary activities (He & Donmez, 2018). Anticipatory driving, although arguably an important skill for supervising level-2 vehicles, has not yet been studied for automated vehicles.  This project aims to investigate (1) how automation affects anticipation in driving and (2) what type of feedback can be effective to support driver anticipation in automated vehicles.

Sponsor: NSERC (Discovery Grant)

Student PI(s): Dengbo He

Facilitating anticipatory driving strategies

The project aims to aid in the development of conscious and competent driving skills, specifically with respect to drivers’ abilities to anticipate upcoming events in traffic. By enabling drivers to correctly interpret the current traffic situation and predict probable changes a few seconds ahead, adequate action can be taken early, and positively impact safety and fuel consumption.

The project is characterized by two main objectives, one being the identification of a working definition of anticipatory driving (and in this context, creation of a taxonomy of anticipatory driving and identification of appropriate measures), and the second relating to researching adequate means of supporting anticipatory driving. Our vision here is an in-car interface that provides an augmented, real-time representation of the current traffic situation, and facilitates adequate skill- and rule-based behaviours for anticipatory driving.

In conjunction with Prof. Greg A. Jamieson (University of Toronto)

Sponsor(s): AUTO21

Student PI(s): Patrick Stahl


Drivers’ anticipation behaviours in autonomous vehicles

He D., DeGuzman A. C., Donmez B. (Accepted), Anticipatory driving in automated vehicles: The effects of driving experience and distraction. Human Factors: The Journal of the Human Factors and Ergonomics Society. [pre-print]

He D., Kanaan D., Donmez B. (2021). The effect of driving automation on drivers’ anticipatory glances. International Ergonomics Association 21st Triennial Congress.

He, D., Kanaan, D., & Donmez, B. (2021). In-vehicle displays to support driver anticipation of traffic conflicts in automated vehicles. Accident Analysis and Prevention, 149, 105842. [post-print]

He, Dengbo. (2020). Understanding and Supporting Anticipatory Driving in Automated Vehicles (PhD Thesis). University of Toronto.

He, D., & Donmez, B. (2020). The Influence of Visual-Manual Distractions on Anticipatory Driving. Human Factors.

He, D. & Donmez, B. (2018). The effects of distraction on anticipatory driving. In Proceedings of the Human Factors and Ergonomics Society 62nd Annual Meeting (pp. 1960-1964), Philadelphia, PA. (HFES the Alphonse Chapanis Award Finalist for Best Student Paper, 1 out of 3).