Credibility reduction of Vehicle-to-Everything (V2X) communication would significantly influence the safety and performance of Intelligent Transport System (ITS). For the purpose of interactive information credibility enhancement during V2X communication, this paper first proposes a vehicle state optimization method based on communication delay. Then, considering comprehensive credibility, a vehicle state accuracy evaluating method is presented to judge the optimization results. In particular, Bayesian regularized BP neural network is designed for efficient position prediction with a high level of accuracy achieved. Testing sets composed of realistic data and communication delay error model are established to verify the performance of the proposed optimization method. The results show that, compared to the realistic data, the proposed method is able to reduce the differentials between received vehicle state and realistic vehicle state. Furthermore, the credibility of the communication can be improved and vehicle safety can be guaranteed.

CICTP