Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning

Understand

The behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle.

• However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios.
• In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem.
• Specifically, we used Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) for comparative experiments.