In recent years, electrified mobility (e-mobility), especially connected and autonomous electric vehicles (CAEVs), has been gaining momentum along with the rapid development of emerging technologies such as artificial intelligence (AI) and Internet of Things. The social benefits of CAEVs are manifested in the form of safer transportation, lower energy consumption, and reduced congestion and emissions. Nevertheless, it is highly difficult to design driving policies that ensure road safety, travel efficiency, and energy conservation for all CAEVs in traffic flows, particularly in a mixed-autonomy scenario where both CAEVs and human-driven vehicles (HDVs) are on the road and interact with each other. Here we present a novel deep multiagent reinforcement learning (DMARL)-enabled energy-aware cooperative driving solution, facilitating CAEVs to learn vehicular platoon management policies for guaranteeing overall traffic flow performance. Specifically, with the aid of information communication technology (ICT), CAEVs can share their vehicle state and learned knowledge, such as their state of charge (SoC), speed, and driving policies. Additionally, a cooperative multiagent actor–critic (CMAAC) technique is developed to optimize vehicular platoon management policies that map perceptual information directly to the group decision-making behaviors for the CAEV platoon. The proposed approach is evaluated in highway on-ramp merging scenarios with two different mixed-autonomy traffic flows. The results demonstrate the benefits of our scheme. Finally, we discuss the challenges and potential research directions for the proposed energy-aware cooperative driving solution.

中文翻译：

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迈向智能互联电动汽车：利用深度多智能体强化学习的能源感知协同驾驶

近年来，随着人工智能（AI）和物联网等新兴技术的快速发展，电动汽车，特别是联网自动驾驶电动汽车（CAEV）不断发展。CAEV 的社会效益体现在更安全的交通、更低的能源消耗、减少拥堵和排放等方面。然而，设计确保交通流中所有 CAEV 的道路安全、行驶效率和节能的驾驶政策非常困难，特别是在 CAEV 和人类驾驶车辆 (HDV) 都在路上的混合自动驾驶场景中并互相互动。在这里，我们提出了一种新颖的深度多智能体强化学习（DMARL）支持的能量感知协作驾驶解决方案，帮助CAEV学习车辆编队管理策略，以保证整体交通流性能。具体来说，借助信息通信技术（ICT），CAEV可以共享车辆状态和学到的知识，例如充电状态（SoC）、速度和驾驶策略。此外，还开发了一种合作性多智能体行为评论家 (CMAAC) 技术来优化车辆排管理策略，将感知信息直接映射到 CAEV 排的群体决策行为。所提出的方法在具有两种不同混合自主交通流的高速公路入口匝道合并场景中进行了评估。结果证明了我们计划的好处。最后，我们讨论了所提出的能源感知协同驾驶解决方案的挑战和潜在的研究方向。