具有不确定交通信号定时的鲁棒最优节能驾驶控制
Robust Optimal Eco-driving Control with Uncertain Traffic Signal Timing
AI总结：
本文提出一种考虑多个信号交叉口的鲁棒最优节能驾驶控制策略，通过空间车辆速度剖面优化最小化全局燃油消耗，引入有效红灯持续时间概念以描述通过信号交叉口的可行通过时间，并通过动态规划求解最优控制问题，仿真结果表明该方法能有效降低燃油消耗。
摘要：
本文提出了一种考虑多个信号化交叉口的鲁棒最优节能驾驶控制策略，开发了空间车辆速度剖面优化公式以最小化全局燃油消耗，将驾驶时间作为其中一个状态变量。引入有效红灯持续时间（ERD）作为随机变量以描述通过信号化交叉口的可行通过时间，并在最优控制问题中加入机会约束以考虑不确定的信号定时。通过动态规划（DP）求解最优节能驾驶控制问题。仿真结果表明，与改进的智能驾驶员模型相比，最优节能驾驶可将燃油消耗减少50-57%，同时保持到达时间相同。鲁棒性公式在面对不确定的信号定时时显著减少交通交叉口违规，尽管在燃油经济性上略逊于非鲁棒方法。
英文摘要：
This paper proposes a robust optimal eco-driving control strategy considering multiple signalized intersections with uncertain traffic signal timing. A spatial vehicle velocity profile optimization formulation is developed to minimize the global fuel consumption, with driving time as one state variable. We introduce the concept of effective red-light duration (ERD), formulated as a random variable, to describe the feasible passing time through signalized intersections. A chance constraint is appended to the optimal control problem to incorporate robustness with respect to uncertain signal timing. The optimal eco-driving control problem is solved via dynamic programming (DP). Simulation results demonstrate that the optimal eco-driving can save fuel consumption by 50-57% while maintaining arrival time at the same level, compared with a modified intelligent driver model as the benchmark. The robust formulation significantly reduces traffic intersection violations, in the face of uncertain signal timing, with small sacrifice on fuel economy compared to a non-robust approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
