追溯自动驾驶系统撞车率分析的基准,使用警方报告的撞车数据
John M Scanlon1, Kristofer D Kusano1, Laura A Fraade-Blanar1
1Waymo, LLC, Mountain View, CA.
Traffic injury prevention
|November 1, 2024
概括
研究人员正在开发新的方法来准确评估自动驾驶系统 (ADS) 的安全性. 这项研究为创建可靠的撞车率基准来比较ADS性能与人类驾驶员提供了指导.
科学领域:
- 道路交通安全问题 道路安全问题
- 自动驾驶系统 (ADS) 是一种自动驾驶系统.
- 车辆安全评估 车辆安全评估
背景情况:
- 自动驾驶系统 (ADS) 正在扩大,需要强大的安全评估方法.
- 当前的安全评估经常将ADS与人驾驶车辆进行比较,需要准确的基准数据.
- 现有的基准方法在数据可比性和潜在偏差方面面临挑战.
研究的目的:
- 解决和扩展现有的关于自动驾驶系统 (ADS) 安全影响的文献.
- 通过使用警方报告的数据来生成ADS部署的地区的人类机率.
- 为ADS安全基准确定提供可重复和透明的方法.
主要方法:
- 利用公开可访问的,警方报告的撞车数据.
- 应用不足报告的校正方法,以提高数据可比性.
- 分析事故发生率与地理区域,道路类型和车辆类型之间的关系.
主要成果:
- 确定了生成可靠的ADS安全基准的基本步骤.
- 展示了如何未能考虑碰撞率依赖性可能会导致ADS比较偏差.
- 在现有ADS基准文献的背景下提出分析.
结论:
- 为ADS评估创建准确的碰撞率基准提出了重大挑战.
- 研究人员在选择和生成基准数据时必须谨慎.
- 这项工作提供了分析指导,以促进对准确的ADS基准估计的共识.
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