Abstract: Aiming at the influence of vehicle speed on safety and comfort when changing lanes autonomously, this paper proposes an optimal trajectory generation algorithm based on weight adaptation, the optimal trajectory is generated and selected by using a loss function weight adaptation method based on real-time vehicle speed and recursive least squares algorithm. In order to verify the effect of the method, this paper adopts the Unity3D simulation environment based on PhysX dynamics engine to construct a simulation environment based on 3D scenes, and ensures that the parameters of the control module and the planning algorithm are consistent in the simulation system, which is used for evaluating the effect of the weight-adaptive algorithm relative to the original algorithm in a variety of scenes. The experimental results show that the self-driving car can realize trajectory planning that meets the requirements of safety, smoothness and comfort indexes through the adaptive updating method of loss function weights, and the trajectory generated by the adaptive algorithm is smoother and more comfortable compared with the original algorithm. The research in this paper helps to improve the safety, smoothness and comfort of autonomous lane changing, and provides a research basis for the landing of autonomous driving in high-speed scenarios.

Key words: path planning, loss function, adaptive, recursive least squares algorithm

摘要： 针对自主换道时车速对于安全性与舒适性的影响，提出了一种基于权重自适应的路径规划优化算法，即采用基于实时车速和递归最小二乘算法的损失函数权重自适应方法生成与选择最优轨迹。为了验证该方法的效果，采用基于PhysX动力学引擎的Unity3D仿真环境，构建基于3D场景的仿真环境，在仿真系统中保证控制模块与规划算法参数一致，用于多种场景下评价权重自适应算法相对于原始算法的效果。实验结果表明，自动驾驶汽车通过损失函数权重的自适应更新方法，可以实现满足安全性、平滑性和舒适性指标要求的轨迹规划，且自适应算法生成的轨迹相较于原始算法更加平滑舒适。该研究有助于提升自主换道的安全性、平滑性和舒适性，为高速场景下自动驾驶的落地提供了研究基础。

关键词: 路径规划, 损失函数, 自适应, 递归最小二乘法