研究自动驾驶场景参数化如何影响评估结果，提醒谨慎设计测试条件。
Scenario-based assessment of automated driving systems: How (not) to parameterize scenarios?
- 用人类驾驶员模拟对比不同参数化场景的测试表现
- 发现参数设置显著影响碰撞可避免性判断的准确率
- 建议改进现有法规中的场景设定方式，适合交通系统评估者
自动驾驶系统（ADS）发展迅速，联合国第157号法规（UN R157）于2021年通过，要求自动化车道保持系统（ALKS）避免所有合理可避免的碰撞。该法规采用“熟练且专注的人类驾驶员”模拟性能作为区分可避免与不可避免碰撞的标准，并基于三种预设场景类型进行基准测试。然而，这些场景的参数化设定未加细化，本文研究发现参数选择对仿真结果有显著影响。通过对比真实世界数据与参数化场景，结果显示影响程度取决于场景类型、驾驶员模型和评估指标。文中提出若干替代参数化方案，使召回率、精确率和F1分数更接近非参数化场景结果。研究强调需审慎设计场景参数，并建议优化现行UN R157的评估方法。
原文摘要 · Abstract (English)
The development of Automated Driving Systems (ADSs) has advanced significantly. To enable their large-scale deployment, the United Nations Regulation 157 (UN R157) concerning the approval of Automated Lane Keeping Systems (ALKSs) has been approved in 2021. UN R157 requires an activated ALKS to avoid any collisions that are reasonably preventable and proposes a method to distinguish reasonably preventable collisions from unpreventable ones using "the simulated performance of a skilled and attentive human driver". With different driver models, benchmarks are set for ALKSs in three types of scenarios. The three types of scenarios considered in the proposed method in UN R157 assume a certain parameterization without any further consideration. This work investigates the parameterization of these scenarios, showing that the choice of parameterization significantly affects the simulation outcomes. By comparing real-world and parameterized scenarios, we show that the influence of parameterization depends on the scenario type, driver model, and evaluation criterion. Alternative parameterizations are proposed, leading to results that are closer to the non-parameterized scenarios in terms of recall, precision, and F1 score. The study highlights the importance of careful scenario parameterization and suggests improvements to the current UN R157 approach.
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