Cybersecurity for mixed-autonomy traffic: detection and localization of compromised vehicles
Principal Investigator(s):Raphael Stern, Assistant Professor, Civil, Environmental and Geo-Engineering
Automated vehicles provide new opportunities for malicious actors to compromise vehicle security. While obvious hacks that cause crashes may be easy to identify and isolate, other vehicle compromises may be more difficult to identify, especially if the hacks changing vehicle driving behavior are more subtle. Such a hack could be introduced to automated or partially automated vehicles--such as adaptive cruise control vehicles--via a malicious software update and go undetected. However, even subtle changes to driving behavior may have widespread disruption to the transportation network by seeding new traffic jams causing delay and excess fuel consumption and emissions. For example, if such a hack were released on all vehicles of a specific make and model, even just slightly more aggressive driving could cause a network-wide increase in delays.
In this project, researchers will use trajectory anomaly detection to identify compromised vehicles based on their driving behavior and the collective traffic dynamics. Specifically, the research team will use car-following models to simulate traffic flow both of typical mixed autonomy traffic as well as traffic where some of the automated vehicles have been compromised. Then, using this synthetic traffic data, researchers will use anomaly detection in the form of neural networks and autoencoders to identify atypical traffic conditions where traffic state evolution does not follow typical traffic dynamics. This project will couple traffic flow theory and emerging methods in deep neural networks to conduct the first step in a larger effort to secure our transportation infrastructure against cyber threats.