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，然而大多数生态驾驶策略仅关注单车道的纵向速度控制。这忽略了横向决策（如变道）对整体能效的重要影响，尤其是在存在交通信号灯和异质交通流的环境中。为弥补这一不足，我们提出了一种新颖的节能运动规划框架，该框架利用车路协同（V2I）通信技术，联合优化纵向速度与横向变道决策。我们的方法通过基于图的近似来估计长期能耗成本，并在交通约束下求解短时域最优控制问题。通过使用基于实际纯电动汽车标定的数据驱动能耗模型，我们在硬件在环实验中证明，与人类驾驶员相比，该方法可将运动能耗降低高达24%，凸显了网联赋能规划对实现可持续城市自动驾驶的潜力。