Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications

TL;DR

This paper introduces a transferable deep reinforcement learning framework for autonomous vehicles equipped with joint radar-communications, enabling safer and more efficient operation in dynamic environments by optimizing decision-making without prior environmental knowledge.

Contribution

The work presents a novel transfer learning-based deep reinforcement learning framework that enhances decision-making for AVs with JRC functions, improving scalability and training efficiency in new environments.

Findings

Reduces obstacle miss detection probability by up to 67%.

Outperforms conventional deep reinforcement learning approaches.

Enables AVs to adapt quickly to new environments.

Abstract

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.