Ensuring information integrity in V2X cooperative perception systems for multi-agent autonomous vehicles

Cooperative perception based on Vehicle-to-Everything (V2X) technologies is effective methodology that allows the use of various data sources, such as sensors, cameras, and sensors, enabling unmanned vehicles to exchange data to overcome the limitations of individual perception. However, the integration of multi-agent data systems creates additional vector attack in cooperative perception and information integrity protection. A quantitative metric of information integrity I_A(tk), is introduced, based on pairwise consistency of agent observations using Mahalanobis distance. A threat model is developed that classifies types of attacks in V2X systems. A model has been developed that includes the state of agent xi(t) = (pi(t), vi(t), θi(t)), local perception Oi ii(t), dynamic connectivity graph G(t), and the proposed metric I_A(tk), has been described, which quantitatively assesses the integrity of information exchange. The proposed method surpasses analogues in key metrics: area Under the ROC curve (Receiver Operating Characteristic), (Area Under the Curve, AUC) = 0.94 (95 % confidence interval [0.92; 0.96]) versus AUC = 0.87 for the best competitor (MATE, Multi-Agent Trust Estimator; DeLong criterion, p less than 0.05), where p is the achieved level of significance of the statistical criterion. Further prospects and areas of application of the model will focus on conducting experimental validation of the proposed structure based on the V2X-Seq dataset. Carrying out integration with formal verification through the Hamilton–Jacobi equation, performing reachability analysis to ensure security guarantees, introducing trust assessments with adaptive component weights to identify specific Byzantine agents.