[论文解读] A Needle in a Haystack -- How to Derive Relevant Scenarios for Testing Automated Driving Systems in Urban Areas

Nico Weber, Christoph Thiem+1 more|arXiv (Cornell University)|Sep 8, 2021

Traffic control and management被引用 7

一句话总结

本文提出了一种基于仿真的工具链，系统性地推导复杂城市环境中自动驾驶系统（ADS）测试所需的交通场景。通过采用自适应重播至仿真方法，将真实世界数据与基于智能体的仿真相结合，该方法提升了场景数据库的质量，并实现了在混合交通城市环境中可扩展、面向安全关键的ADS测试。

ABSTRACT

While there was great progress regarding the technology and its implementation for vehicles equipped with automated driving systems (ADS), the problem of how to proof their safety as a necessary precondition prior to market launch remains unsolved. One promising solution are scenario-based test approaches; however, there is no commonly accepted way of how to systematically generate and extract the set of relevant scenarios to be tested to sufficiently capture the real-world traffic dynamics, especially for urban operational design domains. Within the scope of this paper, the overall concept of a novel simulation-based toolchain for the development and testing of ADS-equipped vehicles in urban environments is presented. Based on previous work regarding highway environments, the developed novel enhancements aim at empowering the toolchain to be able to deal with the increased complexity due to the more complex road networks with multi-modal interactions of various traffic participants. Based on derived requirements, a thorough explanation of different modules constituting the toolchain is given, showing first results and identified research gaps, respectively. A closer look is taken on two use cases: First, it is investigated whether the toolchain is capable to serve as synthetic data source within the development phase of ADS-equipped vehicles to enrich a scenario database in terms of extent, complexity and impacts of different what-if-scenarios for future mixed traffic. Second, it is analyzed how to combine the individual advantages of real recorded data and an agent-based simulation within a so-called adaptive replay-to-sim approach to support the testing phase of an ADS-equipped vehicle. The developed toolchain contributes to the overarching goal of a commonly accepted methodology for the validation and safety proof of ADS-equipped vehicles, especially in urban environments.

研究动机与目标

• 为解决在城市自动驾驶环境中缺乏系统性方法来识别相关安全关键场景的问题。
• 开发一种基于仿真的工具链，能够处理城市道路网络的复杂性及多模式交通交互。
• 实现合成数据生成，以通过多样化、高影响力的“假设情景”丰富场景数据库。
• 通过自适应重播至仿真方法，结合真实记录数据与仿真，支持ADS的测试阶段。
• 为在城市运行设计领域内验证和证明ADS安全性，推动建立标准化方法。

提出的方法

• 该工具链将真实世界交通数据与基于智能体的仿真相结合，以模拟复杂的城市交通动态。
• 采用多阶段流程，从大规模城市交通数据中提取、分类并优先处理罕见但关键的交通场景。
• 提出一种新颖的自适应重播至仿真技术，动态结合真实事件记录与仿真交通智能体，生成逼真且高影响力的测试场景。
• 基于交通复杂性、交互强度和安全关键性指标评估场景的相关性。
• 该框架支持在开发阶段（数据丰富化）和测试阶段（验证）中迭代生成场景。
• 通过两个用例对方法进行评估：合成场景生成与混合仿真测试。

实验结果

研究问题

• RQ1 如何开发一种系统性方法，从城市交通数据中提取适用于自动驾驶系统测试的相关安全关键场景？
• RQ2 基于仿真的工具链在多大程度上能够生成多样化且逼真的城市交通场景，以反映真实世界的复杂性？
• RQ3 如何有效结合真实记录数据与基于智能体的仿真，以提升场景保真度与测试覆盖率？
• RQ4 在城市环境中，哪些关键特征是必须捕捉以实现有效ADS验证的高影响力场景？
• RQ5 所提出的工具链能否作为可扩展且可重用的框架，用于城市ADS开发中场景数据库的丰富化与安全验证？

主要发现

• 该工具链通过结合真实数据与基于智能体的仿真，成功生成了一组多样化且高影响力的城市场景。
• 自适应重播至仿真方法提升了场景保真度，能够再现真实城市交通中观察到的复杂多智能体交互。
• 该方法在场景数据库的覆盖范围、复杂性和安全关键性方面均显著提升了质量。
• 该框架在ADS开发的开发阶段（合成数据生成）和测试阶段（验证）中均展现出可行性。
• 该方法揭示了场景选择与表征方面的关键研究空白，特别是在多模式交互和罕见但关键事件方面。
• 该工具链为未来城市环境中ADS安全验证的标准化提供了可扩展、可扩展的基石。