Abstract:

Due to the complicated and volatile traffic scenes， deep learning-based approaches and most of the deep reinforcement learning approaches cannot satisfy the requirements of real applications. To address these issues， a reinforcement learning-based approach based on multi-sensing and multi-constraint reward function under SAC framework（MSMC-SAC） is proposed. The inputs of the method include front images and LiDAR data， as well as the bird's-eye view information generated from the perception results. The multiple information input is coded by an encoding network to obtain the representation in latent space， and the reconstructed information is used as the input for reinforcement learning module， and a reward function considering various constraints such as transverse-longitudinal error， heading， smoothness， and driving speed is designed. The performance of the proposed method in some typical traffic scenarios is simulated and verified with CARLA. The multi-constraint reward mechanism is analyzed. The simulation results show that the presented approach can generate the driving policies in many traffic scenarios， and the performance is outperformed against the existing SOTA methods.

Key words: vehicle engineering, deep reinforcement learning, driving policy, multi-reward function