Cooperative Adaptive Cruise Algorithm Based on Trajectory Prediction for Driverless Buses

Abstract

Cooperative adaptive cruise control (CACC) technology offers a proven solution to the current traffic congestion problems caused by the yearly growth of car ownership. Coping with random lane changes of bypass vehicles under the condition of traffic congestion is a challenge for urban driverless vehicles. In this paper, to meet the demand for high comfort driverless buses driving on urban roads, an active anti-disturbance following control method for driverless buses based on bystander vehicle intention recognition and trajectory prediction is proposed for the scenario of bystander vehicle cut-in during driving to alleviate the disturbance caused by bystander vehicles, improve passenger comfort, and suppress multi-vehicle oscillation. The simulation results show that the intelligent prediction system-based queue reduces the traffic oscillation rate by an average of 9.8% and improves the comfort level by an average of 11% under side-car insertion conditions. The results of the real vehicle test show that the vehicles based on the intelligent prediction algorithm have a 25.5% reduction in maximum speed adjustment, 14.5 m average reduction in following distance, 6% improvement in comfort, and 27% improvement in rear vehicle comfort.