Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24 percent compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy.

翻译：网联自动驾驶车辆（CAVs）在城市驾驶中具有节能潜力，但现有生态驾驶策略大多仅关注单车道的纵向速度控制，忽略了变道等横向决策对整体能量效率的重要影响，尤其是在存在交通信号灯和异质交通流的环境中。为填补这一空白，本文提出一种新颖的能量感知运动规划框架，利用车路协同（V2I）通信技术联合优化纵向速度与横向变道决策。该方法通过基于图的近似估计长期能量成本，并在交通约束下求解短时域最优控制问题。基于实际纯电动汽车标定的数据驱动能量模型，我们通过硬件在环实验证明：相较于人类驾驶员，本方法可降低高达24%的运动能耗，凸显了网联化规划对可持续城市自动驾驶的潜力。