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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.