Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications
This work addresses safety and efficiency for autonomous vehicles by improving radar-communication integration, though it appears incremental as it builds on existing deep reinforcement learning techniques.
The authors tackled the challenge of optimizing Joint Radar-Communications functions for autonomous vehicles in dynamic environments by proposing a deep reinforcement learning framework with transfer learning, which reduced obstacle miss detection probability by up to 67% compared to conventional methods.
Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.