A Virtual Models-Based CAVs Platoon Resilient to Network and Sensor Attacks

The most typical topological structure of Connected and Autonomous cars (CAVs) is a platoon formation, in which vehicles move closely together, following the leader’s instructions, and offer autonomous and intelligent transport services. Nevertheless, several attacks that inject false data into networks and onboard sensors may endanger platoon stability, resulting in severe collisions over road networks in the worst case. This paper proposes a mechanism for cooperative CAV platoon recovery in the face of false data injection attacks called PReCAV. PReCAV employs virtual and physical system models to maintain the resilience of the CAV platoon despite network and sensor attacks. An evaluation by NS3 and SUMO simulations showed that PReCAV provided stability and resilience to the CAV platoon formation in a representative scenario face to false data injection attacks in the network and the sensors. For injecting false data into the network and sensors, we defined an attack model that manipulates information, such as speed and localization, using a uniform distribution to vary the falsification size over time, that is, how far does the information value vary from the correct one. During network and sensors attacks, PReCAV showed average values of 0.16 m/s2 acceleration, 21.76 m/s of speed, and 28.35 m of the distance between vehicles in the platoon when the desired speed was 22 m/s. Meanwhile, for a desired speed of 15 m/s, PReCAV maintained average values of 0.11 m/s2, 15.26 m/s, and 22.42 m in the platoon. Those results demonstrate the ability of PReCAV to maintain vehicle dynamics in the platoon in the face of attacks.