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：本文在包含人工驾驶和自动驾驶车辆的混合交通环境中，将帕累托最优概念应用于换道运动规划。首先，建立多目标优化问题，联合建模换道车辆及其周围车辆的舒适性、效率和安全性。随后，通过采用NSGA-II（非支配排序遗传算法-II）获得帕累托最优解。最后，实验部分从帕累托最优视角分析了宏观和微观的换道影响，并进行了全面的敏感性分析。结果表明，我们的算法能够显著降低其影响区域内的换道干扰，总成本减少范围为10.94%至48.66%。本文可视为帕累托最优概念应用的一个初步研究框架，期望该研究能为自动驾驶技术提供有价值的见解。