Collaborative Study of Decision-making and Trajectory Planning for Autonomous Driving Based on Soft Acto-Critic Algorithm

To improve the learning speed, safety and rationality of autonomous driving decision-making, this paper proposed a collaborative method of autonomous driving decision-making and planning based on Soft Actor-Critic (SAC) algorithm. The autonomous driving decision planning collaborative agent was designed by introducing the SAC algorithm with the rule-based decision planning method. Combined with the Self-Attention Mechanism (SAM) and the Gated Recurrent Unit (GRU), a preprocessing network was constructed to improve the agent's ability to understand traffic scenarios and improve the agent's learning speed. Considering the specific implementation mode of the planning module, the study used the action space to improve the executability of the decision- making results. The reward function was designed by using the information feedback, adding the constraints of vehicle driving conditions to the agent, and transmitting the trajectory information to the decision-making module. The collaboration of decision-making and planning improved the safety and rationality of decision-making. The dynamic traffic scenarios were built in the CARLA autonomous driving simulation platform to train the agent, and the proposed decision-making and planning collaboration method was compared with the conventional decision-making planning method based on SAC algorithm in different scenarios. The experimental results showed that the learning speed of the agent designed in this paper had increased by 25.10% . The average vehicle speed generated by its decision outcomes was higher and closer to the expected road speed. The speed variation rate produced by its decision outcomes was smaller, and the path length and curvature variation rate resulting from its decision outcomes were also smaller compared to traditional methods. © 2024 Science Press. All rights reserved.