動的環境下でレーダー通信統合機能を持つ自動運転車のための転移可能な深層強化学習フレームワーク
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications
レーダー検出とデータ通信を統合した自動運転車が動的で不確実な環境で最適に機能選択できるよう、MDPに基づく深層強化学習と転移学習を組み合わせた枠組みを提案し、障害物の見逃し検出率を最大67%低減した。
著者: Nguyen Quang Hieu, Dinh Thai Hoang, Dusit Niyato, Ping Wang, Dong In Kim, Chau Yuen
分類: cs.LG, cs.RO
原文アブストラクト
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.
