Cyberattacks on connected automated vehicles: A traffic impact analysis

Abstract

Cybersecurity has become one of the major challenges for Connected Automated Vehicles. Previous work in this field mostly focused on the detection of and defence against Connected Automated Vehicle-related cyberattacks. Using performance measures collected from traffic micro-simulations, the study analysed the traffic impacts of cyberattacks on Connected Automated Vehicles. Two types of adversary models, namely, time-delay attacks and disturbance attacks, were applied to the simulated traffic on freeway segments and un-signalised intersections, respectively. The effects of various cyberattacks were evaluated based on safety indicators, including Time-To-Collision, Deceleration Rate to Avoid Collision, and efficiency indicators, including speed and flow-density diagrams. The results revealed that both attacks increase the risk and severity of collisions on freeway segments and un-signalised intersections. Location-based time-delay attacks will result in significant deceleration, congestion and reduction in road throughput. Disturbance attacks not only cause congestion but also result in frequent acceleration/deceleration and uneven distribution of traffic density. The impacts of attacks are more severe on heavy traffic. At un-signalised intersections, location-based disturbance attacks lead to a significantly increased risk of right-angled collisions. The results could help better understand the effects of Connected Automated Vehicle-related cyberattacks and shed light on proactive defence against such attacks.