A Privacy-Preserving-Based Distributed Collaborative Scheme for Connected Autonomous Vehicles at Multi-Lane Signal-Free Intersections

This paper proposes a privacy-preserving distributed collaboration (PPDC) scheme for connected autonomous vehicles (CAVs) to cross signal-free intersections based on the cloud, while securing the private data of the vehicles. Firstly, this paper converts the cooperation problem into a multi-objective problem that aims to improve the efficiency of traffic and fuel economy. Secondly, to prevent the privacy of the transmitted data of vehicles from being inferred by untrusted cloud servers or external attackers, an affine masking-based privacy strategy is designed. Specifically, the vehicle first uploads the encrypted state data to the cloud with the affine masking method. Then the cloud returns the control input by solving the newly constructed optimization problem, which is different but equivalent to the original problem. Then the vehicle calculates the real control input by the inverse affine masking mechanism. Simulation examples show that the proposed PPDC scheme can guarantee collision avoidance and the privacy protection of transmitted data of CAVs, improve traffic efficiency as well as fuel economy, and avoid extensive computation burden.