This paper applies the pareto-optimal concept to LC (lane-changing) motion planning in the presence of mixed traffic including manual and autonomous vehicles. Firstly, a multiobjective optimization problem is presented, in which the comfort, efficiency and safety of the LC vehicle and the surrounding vehicles are jointly modelled. Thereafter, the pareto-optimal solutions are obtained through employing the NSGA-II (Non-dominated Sorting Genetic -II) algorithm. Finally, the experiment section analyzes the (macroscopic and microscopic) lane-changing impact from a pareto-optimal perspective. Also, a comprehensive sensitivity analysis is conducted. Our results demonstrate that our algorithm could significantly reduce the lane-changing impact within its region, and the total costs are reduced in the range of 10.94% to 48.66%. This paper could be considered as a preliminary research framework for the application of the pareto-optimal concept. We hope this research will provide valuable insights into autonomous driving technology.
翻译:本文将Pareto最优概念应用于混合交通(包括手动和自动车辆)中的道路切换规划。首先,提出一个多目标优化问题,综合建模了LC车辆和周围车辆的舒适度、效率和安全性。然后,通过采用NSGA-II(非支配排序遗传 - II)算法获取Pareto最优解。最后,实验部分从Pareto最优的角度分析了(宏观和微观)车道切换的影响。还进行了全面的敏感性分析。我们的研究结果表明,我们的算法能够显著减少其区域内车道切换的影响,总费用降低的范围为10.94%至48.66%。本文可作为Pareto最优概念应用的初步研究框架。我们希望这项研究能为自动驾驶技术提供有价值的洞察。