Analysis of the impact of cyberattacks on the lane changing behavior of connected automated vehicles

The characteristics of connected automated vehicles (CAVs) make them more susceptible to various cyberattacks, resulting in traffic congestion, delays, and even serious traffic accidents. In recent years, the safety issues of CAVs have become a hot research topic. The current research mostly focuses on studying and evaluating the impact of cyberattacks on the car-following behavior of single lane vehicles. However, lane changing behavior is also one of the most common traffic behaviors on the road, and compared to car-following behavior, lane changing is more complex, vehicles are more likely to collide with surrounding vehicles, and traffic accidents are more likely to occur. Therefore, it is necessary to explore the impact of cyberattacks on vehicles in lane changing environments. The purpose of this study is to investigate the effect of cyberattacks on the lane-changing behavior of two-lane vehicles and evaluate the security. First, this study establishes a framework for dynamics under cyberattacks by combining the intelligent driver model (IDM) and the minimizing overall braking induced by lane changes (MOBIL) model. Then, the traffic effects of different severity of attacks on the vehicle are simulated under three attack scenarios (bogus information, replay/delay and collusion cyberattacks), respectively. Finally, we use the conflict measures to analyze the collision risk of various cyberattacks, including time-to collision (TTC), time-integrated time-to-collision (TIT) and the standard deviation (SD) of velocity. The simulation results indicate that different cyberattacks may cause changes in lane changing time and refuse to change lanes. In addition, the CAV platoon of two lanes will lead to varying degrees of speed oscillations and collision risks. Among these, collusion attacks cause the most serious damage. These findings help to better understand the impact of cyberattacks on CAVs and lay the foundation for enhanced cybersecurity.