Optimized Trajectory Planning to Reduce Electric Vehicle Energy Consumption in Autonomous Intersection Management

The emergence of new technologies, particularly connected automated vehicles (CAVs), offers potential solutions to improve efficiency, reduce congestion, and enhance road safety, especially at intersections. In recent years, various intersection management schemes have been proposed to accommodate a 100% CAV penetration rate. With so-called autonomous intersection management (AIM), vehicles estimate their arrival time at the intersection and communicate it to other vehicles or a central control system. Conflicts are resolved, and vehicles are assigned specific time slots to safely cross the intersection. While the primary focus of AIM studies has been increasing throughput and reducing delays, the reservation-based paradigm also eliminates the need for complete stops and allows for smooth trajectory planning. This study focuses on optimizing vehicle trajectories in the context of AIM to reduce vehicle energy consumption and driving discomfort. The optimization-based AIM is implemented and tested using a microscopic traffic simulation platform, and a detailed energy consumption model is used to analyze the effects. Energy consumption caused by accelerating at the intersection can be reduced by up to 80%, and maximum acceleration and deceleration values of vehicle trajectories are substantially decreased.