Guzmán Leguel Anthony Kyung, Nguyen Hoa-Hung, Gómez Gutiérrez David, Yoo Jinwoo, Jeong Han-You
Department of Electrical Engineering, Pusan National University, Busan 46241, Republic of Korea.
Intelligent Systems Research Lab, Intel Labs, Intel Tecnología de México, Jalisco 45017, Mexico.
Sensors (Basel). 2024 Sep 20;24(18):6086. doi: 10.3390/s24186086.
Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle's ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL-JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.
车与万物（V2X）通信对于增强车辆网络中的协同感知至关重要。通常，感知被视为车辆感知和共享实时运动信息的能力。我们提出了一种V2X通信中感知的新颖定义，将其概念化为一个多方面的概念，涉及车辆检测、跟踪以及保持它们的安全距离。为了增强这种感知，我们提出了一种用于信标速率和发射功率联合控制的深度强化学习框架（DRL-JCBRTP）。我们的DRL-JCBRTP框架在软演员-评论家（SAC）算法中集成了基于长短期记忆网络（LSTM）的智能体网络和基于多层感知器（MLP）的评论家网络，以有效学习最优策略。DRL-JCBRTP方案利用局部状态信息，使用创新的奖励函数来增加最小感知失败距离。我们的SLMLab-Gym-VEINS模拟表明，DRL-JCBRTP方案在最小化感知失败概率和最大化感知距离方面优于现有信标方案，最终提高了驾驶安全性。
