Highway Decision-Making and Motion Planning for Autonomous Driving via Soft Actor-Critic

In this study, a decision-making and motion planning controller with continuous action space is constructed in the highway driving scenario based on deep reinforcement learning. In the decision-making and planning problem, the goal is to achieve the safety, efficiency, and comfort of automated vehicles. In the driving scenario, the surrounding vehicles are controlled by the intelligent driver model and a general model (minimizing overall braking induced by lane change, MOBIL), which enables them to react to the environment and mimic the vehicle interactions on the highway. Given the uncertainties in the driving conditions, a specific deep reinforcement learning technique, called soft actor-critic, is used to solve the decision-making and planning problem with continuous action space. Simulation results show that the proposed method can solve the decision-making and motion planning problem in the interactive traffic environment to carry out safe lane-change maneuvers and cruise at high speed. In addition, two control policies are developed with different weights on safety, efficiency, and comfort.

Automated summary

The study developed a decision-making and motion planning controller using deep reinforcement learning for highway driving scenario, aiming to achieve safety, efficiency, and comfort of automated vehicles. The proposed method successfully addressed decision-making and planning problems in interactive traffic environment, enabling safe lane changes and high-speed cruising.