Connected and Autonomous Vehicle Trajectory Planning Considering Communication Delay

This paper proposes trajectory planning considering vehicle-to-vehicle (V2V) communication delay on connected and autonomous vehicles (CAVs). Firstly, the major influencing factors of communication delay are analyzed as the mean and standard deviation of probability distributions. Gaussian distribution is used to describe the probability distribution of communication delays. Markov chains are used to describe the probability distributions of delayed state shifts to calculate the probability distributions of associated vehicle trajectories. Secondly, the corridor expansion is performed based on the maximum probability trajectory of the associated vehicles. Therefore, the trajectory planning is constructed based on the corridor expansion of associated vehicles. Improved constraints and objective functions are defined, and experiments are conducted using the quintic polynomial of the Frenet frame. The results showed that the best stability is achieved when the trajectory planning duration is 6.0 s. Meanwhile, the stability of the model is better in all cases when the trajectory frequency is between 0.2 s and 0.5 s. By comparing and analyzing the model in this paper, the traditional human trajectory planning model and the state sampling trajectory planning model, it is found that the model in this paper is the best choice in terms of stability, safety and accuracy.