Robustness of vehicle identification via trajectory dynamics to noisy measurements and malicious attacks

The possibility of using infrastructure-based sensors to identify individual vehicles, and then actuate transportation control infrastructure in response to their individual dynamics will enable to next generation of traffic infrastructure control. However, any such system to identify individual vehicles in the flow will be prone to faulty data or worse, cyberattacks where a malicious actor intentionally injects faulty data. With this context in mind, we investigate the resilience of a recently proposed deep learning based approach to identify individual vehicles in the traffic flow. We conduct numerical experiments where increasing amounts of noise is injected into time series trajectory data and conclude that while the proposed classification method is accurate at identifying individual vehicles when there is no noise, classification accuracy deteriorates quickly when noise is injected.