부산대학교 도서관

Autonomous electric vehicles (AEVs) are revolutionizing the world of smart city transportation due to their low-resource consumption, improved traffic efficiency, zero carbon emissions, and improved road safety. To ensure the safe passage of vehicles through a complex environment, it is essential to plan for safe and smart navigation and energy management for AEVs. This demands an effective model for locating the optimal electric charging stations (ECSs) for scheduling and recharging the AEVs when they run on low battery. Many research works, however, do not focus on navigation and scheduling policies for AEV charging that would occur in extreme events in complex environments. This article puts forth a collaborative optimal navigation and charge planning (CONCP) framework based on multiagent deep reinforcement learning (MADRL). To ensure the safe passage of vehicles through the complex environment, it is essential to plan for safe and smart navigation and energy management for AEVs. The CONCP framework aims to achieve the best route from the origin to the final destination for each AEV, scheduling the optimal ECS while avoiding obstacles, reducing traffic congestion, and maximizing energy efficiency, accordingly. The experimental results indicate that CONCP achieves 27% higher success rates, 31% fewer collision rates, and 37% higher reward per episode than the other state-of-the-art algorithms.