通过车路协同优化车道变换与速度，让电动车城市行驶省电24%。
Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation
- 融合车路通信，联合优化车速与变道决策。
- 实测比人工驾驶节能最高达24%。
- 适合智能网联电动车能量管理研究者。
城市交通中联网自动驾驶车辆（CAVs）有潜力实现节能，但现有生态驾驶策略多仅关注单车道内的纵向速度控制，忽视了横向决策（如变道）对整体能效的影响，尤其是在信号灯和混合交通流环境中。为此，我们提出一种新型的能源感知运动规划框架，利用车路通信（V2I）联合优化纵向速度与横向变道决策。方法采用基于图的近似估算长期能耗，并在交通约束下求解短时最优控制问题。基于实际电池电动车校准的数据驱动能耗模型，通过车在环实验验证，相比人类驾驶员，本方法可将行驶能耗降低最高达24%，凸显了连接性赋能规划在可持续城市自动驾驶中的潜力。
原文摘要 · Abstract (English)
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.
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