Strategic Trust: Reputation-Based Mechanisms for Mitigating Malicious Behavior in Vehicular Collective Perception

Abstract

In the realm of vehicular networks, ensuring the reliability of information exchange is paramount, particularly in scenarios where vehicles rely on cooperative perception for shared situational awareness. This paper addresses the challenge of trust and reliability in such networks, particularly in the face of potential malicious actors. We begin by outlining the problem of unverified cooperative perception messages (CPMs) exchanged between vehicles, highlighting the vulnerability to misinformation and manipulation in the absence of robust trust mechanisms. To tackle this challenge, we propose a game-theoretic framework where vehicles strategically adjust their trust matrices based on past interactions, aiming to optimize trust and reliability in information exchange. Central to our approach is the development of a reputation-based trust mechanism, where vehicles assess the reliability of information received and adjust their trust levels accordingly. We introduce a game framework where vehicles strategically choose between cooperation and modification of relationships based on evolving trust matrices. Through analysis, we identify dominant strategies for information disclosure, leading to a stable equilibrium favoring transparency and truthfulness. Furthermore, we present an innovative trust optimization framework, operationalized through algorithms implemented by both vehicles and a central server. This framework dynamically adjusts trust matrices based on verification outcomes, penalizing uncooperative behavior while rewarding legitimate contributions to the network. Notably, our approach allows for adaptability to different verification mechanisms, enhancing the resilience of the system against diverse forms of malicious behavior.