Comprehensive Analysis of Adaptive Soft Actor-Critic Reinforcement Learning-Based Control Framework for Autonomous Driving in Varied Scenarios

In this article, an energy-efficient autonomous driving framework combining adaptive soft actor-critic (ASAC) evaluation reinforcement learning (RL), and model predictive control (MPC) strategies is proposed. It enables autonomous vehicles (AVs) to obstacle-freely track the desired path that the improved A $^{\ast }$ -based path planner generated. As the core work is to maintain energy-efficient performance, the motor efficiency map is employed to help reduce energy consumption quickly and accurately in an MPC-based predictive path generator and the ASAC-based path-tracking modules. At the same time, the final control input is adapted to the vehicle kinematics, and the generalization performance of the frame is improved. In this work, the CarMaker tool trains and tests on a map of multiple scenarios. Experimental results show that the proposed method is superior to other control methods in path tracking performance, running time, and energy efficiency. Compared to human driving data, this ASAC method can reduce the energy consumption of AVs by 9.432% without any energy feedback mechanism. The method can also deal with various disturbances, such as complex road conditions and vehicle mass changes, and track the path accurately.