Toward Intelligent Connected E-Mobility: Energy-Aware Cooperative Driving With Deep Multiagent Reinforcement Learning

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.