Digital Behavioral Twins for Safe Connected Cars

Abstract

Driving is a social activity which involves endless interactions with other agents on the road. Failing to locate these agents and predict their possible future actions may result in serious safety hazards. Traditionally, the responsibility for avoiding these safety hazards is solely on the drivers. With improved sensor quantity and quality, modern ADAS systems are able to accurately perceive the location and speed of other nearby vehicles and warn the driver about potential safety hazards. However, accurately predicting the behavior of a driver remains a challenging problem. In this paper, we propose a framework in which behavioral models of drivers (Digital Behavioral Twins) are shared among connected cars to predict potential future actions of neighboring vehicles, therefore improving the safety of driving. We provide mathematical formulations of models of driver behavior and the environment, and discuss challenging problems during model construction and risk analysis. We also demonstrate that our digital twins framework can accurately predict driver behaviors and effectively prevent collisions using a case study in a virtual driving simulation environment.