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：本文提出一种面向城市道路网联自动驾驶汽车（CAVs）的新型节能运动规划算法。该方法包含两个组成部分：决策算法和基于优化的轨迹规划器。决策算法利用联网交通信号灯的信号相位与配时（SPaT）信息，以减少能耗为目标选择车道。该算法基于从人类驾驶数据中学习得到的启发式规则。基于优化的轨迹规划器则针对所选车道生成安全、平滑且节能的轨迹。所提策略在车辆在环（VIL）场景下进行实验验证，真实测试车辆接收来自实际与虚拟交通灯的SPaT信息，在测试场地自主行驶，同时通过仿真模拟周围车辆。结果表明，在自动驾驶中运用SPaT信息可提升能效，相较于车道保持算法，所提策略可节省37.1%的能耗。