A Supervision Game-Theoretic Incentive Mechanism for Cooperative Sensing in Connected and Autonomous Vehicles

Cooperative sensing has emerged as a promising approach to enhance the sensing capabilities of connected and autonomous vehicles (CAVs) by enabling them to share sensory data through vehicle-to-vehicle communication. However, the lack of motivation for honest participation and the presence of malicious vehicles hinder the widespread adoption of cooperative sensing. To address these challenges, we propose a novel incentive mechanism based on a supervision-oriented game-theoretic approach. Our framework models the interactions between sensing providers (SPs) and sensing requesters (SRs) as a strategic game, where a supervision mechanism is introduced to monitor and penalize misbehavior. We analyze the Nash equilibrium of the game and derive optimal strategies for both SPs and SRs. Extensive simulations demonstrate the effectiveness of our approach in promoting sensing cooperation, ensuring the utility of participants, and improving the overall performance of cooperative sensing in CAVs compared to existing methods.