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在美国多个密集的城市驾驶环境中,基线脆弱的道路使用者受伤风险

Eamon T Campolettano¹, John M Scanlon¹, Ilan Kadar²

¹Waymo LLC, Mountain View, California.

Traffic injury prevention

|November 1, 2024

概括

了解基线驾驶安全风险是自动驾驶系统 (ADS) 的关键. 这项研究分析了易受伤害的道路使用者 (VRU) 的碰撞,揭示了令人惊的VRU行为和隐蔽的可见性增加了城市地区的受伤风险.

科学领域:

道路安全分析的分析
自动驾驶系统 (ADS) 是一种自动驾驶系统.
弱势道路使用者 (VRU) 的安全.

背景情况:

在城市环境中评估基线驾驶安全风险对于自动驾驶系统 (ADS) 的开发和部署至关重要.
自然驾驶研究为了解现实世界的道路用户互动和碰撞动态提供了有价值的数据.
量化碰撞率,严重程度和脆弱道路使用者 (VRU) 的受伤风险对于安全影响评估至关重要.

研究的目的:

利用自然主义的弱势道路使用者 (VRU) 碰撞数据来量化碰撞率,碰撞严重程度和受伤风险分布.
建立在密集的城市地区驾驶安全风险的基线理解,特别是关于VRU相互作用.
通过提供有关碰撞场景的经验数据,为自动驾驶系统 (ADS) 的安全影响分析提供信息.

主要方法:

分析了335个视频验证的碰撞事件,涉及超过5亿辆车里程的行人,骑自行车和摩托车.
利用匿名的车辆仪表盘摄像头数据,包括视频和传感器信息 (GPS,加速度计).
对碰撞几何和外部因素的定性评估,加上已建立的受伤风险模型 (MAIS2+,MAIS3+) 来估计严重程度.

主要成果:

碰撞速度有所不同,与直行相比,在转时观察到的车辆速度较低.
几乎所有事件 (95%) 的估计最大缩短伤害量 (MAIS) 3+伤害风险低于10%.
较高的伤害风险估计与闭塞和令人惊的VRU行为有关;估计约有55个中度 (MAIS2+) 和6个严重 (MAIS3+) 的伤害.

结论:

令人惊的VRU行为,视力阻塞和车辆行驶动态显著影响碰撞速度和估计的受伤风险.
这些发现为涉及城市环境中的VRU的基线驾驶风险提供了基础的理解.
这项研究作为对VRU碰撞和ADS安全性的更全面分析的起点.

关键词:

在ADS基准测试中使用ADS基准测试.碰撞严重程度的严重程度.累积的伤害风险累积的伤害风险自然主义的驾驶数据.

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