Decision Making for Autonomous Driving Via Multimodal Transformer and Deep Reinforcement Learning

Abstract

On the basis of environmental information processed by the sensing module, the decision module in automatic driving integrates environmental and vehicle information to make the autonomous vehicle produce safe and reasonable driving behavior. Considering the complexity and variability of the driving environment of autonomous vehicles, researchers have begun to apply deep reinforcement learning (DRL) in the study of autonomous driving control strategies in recent years. In this paper, we apply an algorithm framework combining multimodal transformer and DRL to solve the autonomous driving decision problem in complex scenarios. We use ResNet and transformer to extract the features of LiDAR point cloud and image. We use Deep Deterministic Policy Gradient (DDPG) algorithm to complete the subsequent autonomous driving decision-making task. And we use information bottleneck to improve the sampling efficiency of RL. We use CARLA simulator to evaluate our approach. The results show that our approach allows the agent to learn better driving strategies.