Efficient and Eco Lane-Changing Trajectory Planning for Connected and Automated Vehicles: Deep Reinforcement Learning-Based Method

A deep reinforcement learning-based method for planning the lane-changing trajectory of connected and automated vehicles (CAVs) is proposed to increase traffic efficiency and reduce fuel consumption. The long-short-term-memory-based twin delayed deep deterministic policy gradient (LSTM-TD3) algorithm is implemented and trained to achieve the optimal longitudinal and lateral lane-changing trajectory. The instantaneous fuel consumption and the desired speed and acceleration difference are used as reward and penalty terms. The effectiveness of the algorithm was verified through real data based typical lane-changing scenarios using CARLA software. The results indicate that the proposed LSTM-TD3-based lane-changing planning method reduced fuel consumption by 6.36% compared to TD3, 9.84% compared to LSTM-DDPG, and 26.31% compared to DDPG. Compared to TD3, LSTM-DDPG, and DDPG, the completion time for lane-changing is reduced by 0.18s, 0.15s and 0.2s, respectively. The success rate of trajectory planning has also increased compared to other algorithms. Furthermore, the results demonstrate the potential of deep reinforcement learning technologies in the control and applications of CAVs.