我们有多确定我们的自动驾驶系统是安全的?
Erwin de Gelder1, Olaf Op den Camp1
1Integrated Vehicle Safety, TNO, Helmond, The Netherlands.
Traffic injury prevention
|June 2, 2023
概括
本研究使用数据驱动,基于场景的方法量化自动驾驶系统 (ADS) 安全风险估计的不确定性. 该方法有助于确定对自动驾驶技术监管合规性的数据和模拟需求.
科学领域:
- 道路安全研究 道路安全研究
- 自动驾驶系统 (ADS) 是一种自动驾驶系统.
- 风险评估方法 风险评估方法
背景情况:
- 欧盟委员会正在为自动驾驶系统 (SAE 3+级) 起草法规.
- 证明剩余风险低于当前标准是ADS合规性的关键挑战.
- 现有的数据驱动,基于场景的风险评估方法由于有限的数据和测试而面临不确定性.
研究的目的:
- 为了解决ADS估计安全风险的不确定性,给定数据和测试限制.
- 开发一种方法来量化ADS安全风险评估中的不确定性.
- 为指导ADS类型批准数据收集和模拟的要求.
主要方法:
- 为了参数化测试场景,利用了大规模的道路场景收集.
- 估计的场景曝光率和参数分布与置信限.
- 使用概率框架进行虚拟模拟和结合结果,以估计剩余风险和不确定性.
主要成果:
- 开发了一个概率框架来估计ADS的剩余风险及其不确定性.
- 提供了死亡率的置信界限,例如,每小时的死亡人数有95%的确定性.
- 用一个案例研究来说明该方法,该研究量化了纵向控制器的风险和不确定性.
结论:
- 拟议的方法量化了ADS安全风险估计中的不确定性,有助于监管合规.
- 该方法可以告知"需要多少数据"或"需要多少模拟".
- 未来的工作包括将这种方法整合到更高层次的ADS类型批准框架中.
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