连接和自动化车辆测试场景库生成方法研究，第一部分：方法论
Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
AI总结：
本文提出了一种系统框架，用于生成连接和自动化车辆（CAVs）的测试场景库（TSLG），考虑不同的操作设计域（ODDs）、CAV模型和性能指标，通过引入新的场景关键性度量标准和多起始优化方法，提高测试效率。
摘要：
在连接和自动化车辆（CAVs）的发展和部署过程中，测试和评估是一个关键步骤，但目前尚无系统框架用于生成测试场景库。本研究旨在提供一种通用框架，用于解决测试场景库生成（TSLG）问题，考虑不同的操作设计域（ODDs）、CAV模型和性能指标。给定一个ODD，测试场景库被定义为一组关键场景，可用于CAV测试。每个测试场景通过新提出的度量标准——场景关键性进行评估，该度量标准可以计算为机动挑战和暴露频率的组合。为了寻找关键场景，设计了辅助目标函数，并应用了多起始优化方法和种子填充技术。所提出的框架在理论上被证明可以以远少的测试次数获得准确的评估结果，与道路测试方法相比。在本研究的第二部分中，通过三个案例研究来演示所提出的方法。基于强化学习的技术被应用于在高维场景下增强搜索方法。
英文摘要：
Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. The proposed framework is theoretically proved to obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed methodologies. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
