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美国多个密集城市驾驶环境中基本脆弱道路使用者伤害风险。

Baseline vulnerable road user injury risk in multiple U.S. dense urban driving environments.

机构信息

Waymo LLC, Mountain View, California.

Nexar, New York, New York.

出版信息

Traffic Inj Prev. 2024;25(sup1):S94-S104. doi: 10.1080/15389588.2024.2364050. Epub 2024 Nov 1.

Abstract

OBJECTIVE

Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data.

METHODS

From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists). Data consisted of anonymized video and sensor data (Global Positioning System and accelerometer) from vehicles equipped with Nexar dash cameras. Each event was qualitatively evaluated to assess the collision geometries and other extrinsic collision factors (e.g., time of day, roadway location, presence of occlusions). Previously published injury risk models were utilized to provide estimates of Maximum Abbreviated Injury Scale MAIS2+ and MAIS3+ injury potential. Collision severity distributions and aggregated injury risk estimates were compared.

RESULTS

Vehicle speeds at the time of impact ranged from 0 to 39 mph, and VRU speeds ranged from 0 to 27 mph. In general, vehicles were traveling at lower speeds at the time of impact when turning in comparison to straight travel. Nearly all events (95%) were associated with MAIS3+ injury risk estimates below 10%. Collisions involving a potential occlusion or the vehicle responding to surprising behavior by the VRU were associated with higher estimates of injury risk than those without occlusion or with the vehicle initiating the conflict. Based on accumulated risk for each event, it can be estimated that approximately 55 persons could be moderately injured (MAIS2+) and approximately 6 persons could be seriously injured (MAIS3+).

CONCLUSION

These data indicate that responding to surprising VRU behavior, having visibility be potentially occluded, and vehicle travel behavior were associated with differences in collision speed and injury risk estimation. The specific comparisons made in this study are not intended to be comprehensive but serve as a starting point for considering baseline driving risk associated with VRU collisions in dense urban areas.

摘要

目的

理解和建模密集城市地区的基线驾驶安全风险是自动驾驶系统(ADS)安全影响分析的关键起点。本研究的目的是利用自然发生的弱势道路使用者(VRU)碰撞数据,在没有客观伤害结果数据的情况下,量化碰撞率、碰撞严重程度和伤害风险分布。

方法

从超过 5 亿英里的行驶里程中,共视频验证和重建了 335 起涉及 VRU 的碰撞事件(126 名行人、144 名骑自行车的人和 65 名骑摩托车的人)。数据包括配备 Nexar 行车记录仪的车辆的匿名视频和传感器数据(全球定位系统和加速度计)。每个事件都进行了定性评估,以评估碰撞几何形状和其他外在碰撞因素(例如,一天中的时间、道路位置、是否存在障碍物)。利用先前发表的伤害风险模型提供最大简略伤害量表 MAIS2+和 MAIS3+伤害潜力的估计值。比较了碰撞严重程度分布和汇总的伤害风险估计值。

结果

碰撞发生时车辆的速度范围为 0 至 39 英里/小时,VRU 的速度范围为 0 至 27 英里/小时。一般来说,车辆在转弯时的碰撞速度比直线行驶时低。近 95%的事件都与 MAIS3+伤害风险估计值低于 10%有关。涉及潜在障碍物或车辆对 VRU 令人惊讶行为做出反应的碰撞与没有障碍物或车辆引发冲突的碰撞相比,伤害风险估计值更高。根据每个事件的累积风险,可以估计大约有 55 人可能会受中度伤害(MAIS2+),大约有 6 人可能会受重伤(MAIS3+)。

结论

这些数据表明,对 VRU 行为的意外反应、潜在的视线被遮挡以及车辆行驶行为与碰撞速度和伤害风险估计值的差异有关。本研究进行的具体比较并非旨在全面,但可作为考虑密集城市地区与 VRU 碰撞相关的基线驾驶风险的起点。

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