A Trust Assessment Method for Intelligent Connected Vehicles Based on Data Consistency Verification Matched With Adaptive Leader Election in a Zero-Trust Framework

The use of intelligent connected vehicles (ICVs) is an emerging concept in transportation systems, where the platoon leader processes various types of information from followers and roadside units to make accurate decisions. However, in complex network environments, the leader election process can become complicated and inefficient due to potential network attacks and abnormal node behaviours. To address this challenge, this paper presents a leader vehicle election mechanism based on dynamic trust assessment within a zero-trust framework. The proposed mechanism comprises two key components: dynamic trust assessment and adaptive leader election. The dynamic trust assessment method involves a trust evaluation mechanism composed of direct trust assessment and recommendation trust assessment based on the state information of vehicle nodes, where the dynamic trust assessment results are adjusted according to node behaviour information. The leader election mechanism involves periodic elections among the vehicle nodes on the basis of the results of the dynamic trust assessment, in which the vehicle node with the most votes is selected as the leader. Through comparative experiments against a full trust scenario, the proposed method demonstrates superior performance in resisting network attacks and identifying abnormal nodes, thereby significantly improving the safety, stability, and operational efficiency of the vehicle platoon.