Our work aims to present a high-performance and modular sampling-based trajectory planning algorithm for autonomous vehicles. This algorithm is tailored to address the complex challenges in solution space construction and optimization problem formulation within the path planning domain. Our method employs a multi-objective optimization strategy for efficient navigation in static and highly dynamic environments, focusing on optimizing trajectory comfort, safety, and path precision. This algorithm was then used to analyze the algorithm performance and success rate in 1750 virtual complex urban and highway scenarios. Our results demonstrate fast calculation times (8ms for 800 trajectories), a high success rate in complex scenarios (88%), and easy adaptability with different modules presented. The most noticeable difference exhibited was the fast trajectory sampling, feasibility check, and cost evaluation step across various trajectory counts. While our study presents promising results, it's important to note that our assessments have been conducted exclusively in simulated environments, and real-world testing is required to fully validate our findings. The code and the additional modules used in this research are publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Motion-Planner.

翻译：本文旨在提出一种面向自动驾驶车辆的高性能模块化采样轨迹规划算法。该算法针对路径规划领域中解空间构建与优化问题建模的复杂挑战而设计，采用多目标优化策略实现在静态与高动态环境下的高效导航，重点优化轨迹舒适性、安全性及路径精度。我们通过1750个虚拟复杂城市与高速公路场景对算法性能及成功率进行了分析。结果表明：计算速度快（800条轨迹仅需8毫秒），复杂场景下成功率高达88%，且易于适配不同模块。最显著的优势体现在不同轨迹数量下的快速轨迹采样、可行性校验及代价评估环节。尽管研究结果令人振奋，但需明确所有评估均在仿真环境中进行，仍需实际测试以全面验证结论。本研究所用代码及附加模块已作为开源软件公开发布，可通过以下链接获取：https://github.com/TUM-AVS/Frenetix-Motion-Planner