Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164292
Title: Transferable deep reinforcement learning framework for autonomous vehicles with joint radar-data communications
Authors: Nguyen, Quang Hieu
Dinh, Thai Hoang
Niyato, Dusit
Wang, Ping
Kim, Dong In
Yuen, Chau
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Nguyen, Q. H., Dinh, T. H., Niyato, D., Wang, P., Kim, D. I. & Yuen, C. (2022). Transferable deep reinforcement learning framework for autonomous vehicles with joint radar-data communications. IEEE Transactions On Communications, 70(8), 5164-5180. https://dx.doi.org/10.1109/TCOMM.2022.3182034
Project: AISG2-RP-2020-019 
RG16/20
A19D6a0053
Journal: IEEE Transactions on Communications
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. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.
URI: https://hdl.handle.net/10356/164292
ISSN: 0090-6778
DOI: 10.1109/TCOMM.2022.3182034
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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