城市道路上联网车辆在定位不确定性下的生态驾驶：数据驱动方法与实验验证
Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification
AI总结：
本文针对城市道路联网车辆在定位不确定性下的生态驾驶问题，提出一种数据驱动MPC方法，通过学习代价函数和约束实现短时域实时速度规划，并在实车实验中实现12%的能效提升。
摘要：
本文研究城市道路上联网车辆在定位不确定性条件下的生态驾驶问题。生态驾驶被定义为在存在一系列交通信号灯的道路上进行纵向速度规划与控制。我们采用数据驱动模型预测控制（MPC）策略来解决该问题。该方法包括从状态-输入数据中学习代价函数（cost-to-go function）和约束条件。代价函数表示从给定状态出发剩余需要消耗的能量，约束条件确保受控车辆在遵守交通法规的同时及时通过即将到来的交通信号灯。由此得到的凸优化问题具有较短的预测时域，适合实时实现。我们通过真实车辆实验验证了所提方法的有效性。车辆实验表明，与传统方法相比，我们的方法在能效方面实现了12%的提升。传统方法通过求解长时域最优控制问题来规划纵向速度，并使用另一个控制器跟踪规划速度。
英文摘要：
This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the given state, and the constraints ensure that the controlled vehicle passes the upcoming traffic light timely while obeying traffic laws. The resulting convex optimization problem has a short horizon and is amenable for real-time implementations. We demonstrate the effectiveness of our approach through real-world vehicle experiments. Our method demonstrates $12\%$ improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.
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