网联车对抗神经网络跟驰模型

In view of the poor real-time performance and safety as the responses of connected and autonomous vehicles (CAVs) to the speed change of leading vehicle and the low stability of CAV platoon under the current mixed traffic flow situation, a generative adversarial nets vehicle following model (GANVFM) composed of generation model and discrimination model is proposed for CAVs. The generation model extracts the vehicle flowing parameters such as the leading vehicle speed, the following vehicle speed and the vehicle spacing to calculate the generated acceleration, while the discrimination model calculates the similarity of the acceleration parameters generated by generation model and updates both the generation and discrimination models by updating function. Then the real-time performance and safety of CAVs and the stability of vehicle platoon are analyzed by using mean square deviation σ for speed and acceleration, rear-end collision predicting factorγ_n and vehicle following state factor φ_n as corresponding indicators. The results show that the GANVFM has the smallest γ_n and σ, and the real-time performance and safety of GANVFM to the speed change of leading vehicle are high. With the increase of the permeability rate δ of CAV_S, theφ_n reduces, the fleet length shortens, and the fleet stability improves.