There is a gap in understanding how drivers mitigate risk imposed by various driving scenarios, particularly with regard to scenarios in which more advanced ADAS/ADS technologies are available.
For this project, our goal is to identify the driving context in which risk mitigation behaviors do occur. We then aim to develop analytical models that can capture and identify changes in driver performance (e.g., speed, deceleration, and lateral variation) and driver behavior (e.g., eyes-off-road time) that are indicative of risk mitigation behavior. The models can then be generalized for multiple contexts and be adaptable to account for future transportation needs.
In other words, our goal is to be able to reliably predict how drivers might mitigate risk (or not) across numerous driving scenarios, and then suggest countermeasures, when necessary, to enhance safety.