AutoWatch: Learning Driver Behavior with Graphs for Auto Theft Detection and Situational Awareness


Millions of lives are lost due to road accidents each year, emphasizing the importance of improving driver safety measures. In addition, physical vehicle security is a persistent challenge exacerbated by the growing interconnectivity of vehicles, allowing adversaries to engage in vehicle theft and compromising driver privacy. The integration of advanced sensors with internet connectivity has ushered in the era of intelligent transportation systems (ITS), enabling vehicles to generate abundant data that facilitates diverse vehicular applications. These data can also provide insights into driver behavior, enabling effective driver monitoring to support safety and security. In this paper, we propose AutoWatch, a graph-based approach for modeling the behavior of drivers, verifying the identity of the driver, and detecting unsafe driving maneuvers. Our evaluation shows that AutoWatch can improve driver identification accuracy by up to 22% and driving maneuver classification by up to 5.7% compared to baseline techniques.

In ISOC Symposium on Vehicle Security and Privacy (VehicleSec ‘24)
Mert D. Pesé
Mert D. Pesé
Assistant Professor

My research interests include automotive security and privacy.