Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer
This addresses the need for integrated driving tasks in autonomous vehicles, though it appears incremental as it combines existing tasks with a transfer approach.
The paper tackles the problem of autonomous driving by developing a vision-based deep reinforcement learning agent that simultaneously performs lane keeping and car following, and demonstrates its capability through Sim2Real transfer in real-world evaluation.
To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamental and well-studied driving ones. However, previous studies have mainly focused on individual tasks, and car following tasks have typically relied on complete leader-follower information to attain optimal performance. To address this limitation, we propose a vision-based deep reinforcement learning (DRL) agent that can simultaneously perform lane keeping and car following maneuvers. To evaluate the performance of our DRL agent, we compare it with a baseline controller and use various performance metrics for quantitative analysis. Furthermore, we conduct a real-world evaluation to demonstrate the Sim2Real transfer capability of the trained DRL agent. To the best of our knowledge, our vision-based car following and lane keeping agent with Sim2Real transfer capability is the first of its kind.