基于事故数据的自动驾驶汽车的切入前碰撞场景的概括
Pingfei Li1,2, Xinyu Zhu1, Yao Ren1
1School of Automobile and Transportation, Xihua University, Chengdu, 610039, China.
Scientific reports
|July 31, 2024
概括
本研究介绍了一种方法,用于测试自动驾驶汽车 (AV) 的高危情景. 新方法在安全测试中显著增加了碰撞率和自动紧急制动 (AEB) 触发率.
科学领域:
- 自主系统 自主系统
- 车辆安全 车辆安全
- 事故重建事故的重建
背景情况:
- 提高自动驾驶汽车 (AV) 测试的安全性和效率至关重要.
- 高风险测试案例对于强大的AV安全验证至关重要.
研究的目的:
- 为自动驾驶安全测试推导一套全面的高危事故前情景.
- 通过专注于关键场景来提高AV测试的有效性和效率.
主要方法:
- 使用重要的采样和组合测试方法.
- 从国家汽车事故深度调查系统 (NAIS) 中的23个高危切入事故场景中提取了2052个高危事故前情景.
主要成果:
- 实现了69.32%的碰撞率,比直接采样方法高出2.92倍.
- 增加了5.8倍的自动紧急制动 (AEB) 触发率,提高了危险场景的覆盖率.
- 与现有协议相比,生成的场景参数具有明显较低的目标车辆速度,时间差距和碰撞时间.
结论:
- 拟议的方法有效地为AV测试生成高危情景.
- 来自事故数据的场景丰富了现有的自动驾驶测试案例选择.
- 该方法提高了自动驾驶汽车测试的安全性和效率.
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