网联与自动驾驶车辆（CAVs）在城市驾驶中具有节能潜力，但现有大多数生态驾驶策略仅关注单车道内的纵向速度控制，忽视了横向决策（如换道）对整体能效的重要影响，尤其在存在交通信号灯和异构交通流的环境中。为弥补这一空白，我们提出一种新型能量感知运动规划框架，利用车路协同（V2I）通信联合优化纵向速度与横向换道决策。该方法采用基于图的近似方法估计长期能量成本，并在交通约束下求解短时域最优控制问题。我们基于实测电池电动汽车标定的数据驱动能量模型，通过车辆在环实验验证表明，相较于人类驾驶员，本方法可降低运动能耗最高达24%，凸显了网联赋能规划在可持续城市自动驾驶中的潜力。

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.