This paper presents a novel energy-efficient motion planning algorithm for Connected Autonomous Vehicles (CAVs) on urban roads. The approach consists of two components: a decision-making algorithm and an optimization-based trajectory planner. The decision-making algorithm leverages Signal Phase and Timing (SPaT) information from connected traffic lights to select a lane with the aim of reducing energy consumption. The algorithm is based on a heuristic rule which is learned from human driving data. The optimization-based trajectory planner generates a safe, smooth, and energy-efficient trajectory toward the selected lane. The proposed strategy is experimentally evaluated in a Vehicle-in-the-Loop (VIL) setting, where a real test vehicle receives SPaT information from both actual and virtual traffic lights and autonomously drives on a testing site, while the surrounding vehicles are simulated. The results demonstrate that the use of SPaT information in autonomous driving leads to improved energy efficiency, with the proposed strategy saving 37.1% energy consumption compared to a lane-keeping algorithm.

翻译：本文提出了一种面向城市道路上网联自动驾驶汽车（CAVs）的新型节能运动规划算法。该方法由两个部分组成：一个决策算法和一个基于优化的轨迹规划器。决策算法利用来自联网交通信号灯的信号相位与配时（SPaT）信息，以选择有助于降低能耗的车道。该算法基于从人类驾驶数据中学习到的启发式规则。基于优化的轨迹规划器则生成一条朝向所选车道的安全、平滑且节能的轨迹。所提出的策略在车辆在环（VIL）环境中进行了实验评估：一辆真实测试车辆接收来自真实及虚拟交通信号灯的SPaT信息，并在测试场地上自主行驶，而周围车辆则通过仿真生成。结果表明，在自动驾驶中利用SPaT信息能够提升能效，与车道保持算法相比，所提策略节省了37.1%的能耗。