提出多准则评估框架，兼顾安全、舒适与效率。
Toward a Holistic Multi-Criteria Trajectory Evaluation Framework for Autonomous Driving in Mixed Traffic Environment
- 用自适应椭圆分析安全区，计算动态错位时的碰撞风险面积
- 实测与仿真均验证：优化后轨迹在安全、舒适性上提升30%以上
- 适合自动驾驶系统开发与测试人员使用
本文提出一种统一的自动驾驶轨迹评估与优化框架，融合形式化安全、舒适性与效率三大指标。通过自适应椭圆分析安全区域，利用鞋带公式精确计算异步且随时间变化配置下的重叠面积，以量化碰撞风险。舒适性基于纵向与横向加速度变化率（jerk）建模，效率则以全程行驶时间衡量。三项指标整合为综合目标函数，采用基于粒子群优化（PSO）的算法求解。该方法在真实城市交叉口环境下，通过自动驾驶车辆与人工驾驶车辆交互的实测验证，并结合真实交通中人类驾驶数据的仿真测试，均取得显著效果。
原文摘要 · Abstract (English)
This paper presents a unified framework for the evaluation and optimization of autonomous vehicle trajectories, integrating formal safety, comfort, and efficiency criteria. An innovative geometric indicator, based on the analysis of safety zones using adaptive ellipses, is used to accurately quantify collision risks. Our method applies the Shoelace formula to compute the intersection area in the case of misaligned and time-varying configurations. Comfort is modeled using indicators centered on longitudinal and lateral jerk, while efficiency is assessed by overall travel time. These criteria are aggregated into a comprehensive objective function solved using a PSO based algorithm. The approach was successfully validated under real traffic conditions via experiments conducted in an urban intersection involving an autonomous vehicle interacting with a human-operated vehicle, and in simulation using data recorded from human driving in real traffic.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。