Advanced driver assistance systems (ADASs) enhance transportation safety and mobility, and reduce impacts on the environment and economical costs, through decreasing driver errors. One of the main features of ADASs is cruise control system that maintains the driver's desired speed without intervention from the driver. Adaptive cruise control (ACC) systems adjust the vehicle's speed to maintain a safe following distance to the vehicle in front. Adding vehicle-to-vehicle and vehicle-toinfrastructure communications (V2X) to ACC systems, result in cooperative adaptive cruise control (CACC) systems, where each vehicle has trajectory data of all other vehicles in the same lane. Although CACC systems offer advantages over ACC systems in increasing throughput and average speed, they are more vulnerable to cyber-security attacks. This is due to V2X communications that increase the attack surface from one vehicle to multiple vehicles. In this paper, we inject common types of attack on the application layer of connected vehicles to show their vulnerability in comparison to autonomous vehicles. We also proposed a decision support system that eliminates risk of inaccurate information. The microscopic work simulates a CACC system with a bi-objective PID controller and a fuzzy detector. A case study is illustrated in detail to verify the system functionality.
This same data is received by other vehicles traveling in the same range REF .
A Decision Support System for Improving Resiliency of Cooperative Adaptive Cruise Control Systems
Procedia Computer Science
Computer Science