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基于中国视频记录的 K-均值聚类方法对典型电动两轮车碰撞前场景的研究。

Study of typical electric two-wheelers pre-crash scenarios using K-medoids clustering methodology based on video recordings in China.

机构信息

School of Aerospace Engineering, Xiamen University, Xiamen, China; School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China.

School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, China; Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle, Xiamen, China.

出版信息

Accid Anal Prev. 2021 Sep;160:106320. doi: 10.1016/j.aap.2021.106320. Epub 2021 Aug 4.

Abstract

Crash safety of electric two-wheelers (ETWs) has been one of the most important safety issues in China due to their high proportion of involvement in traffic accidents. Automated Emergency Braking (AEB) systems have proven to be effective in reducing the number of fatalities and injuries in traffic accidents. Providing test scenarios is one of the fundamental tasks required for establishing a set of AEB test programs for ETWs. Compared to traditional in-depth accident data, accident data accompanied with video recordings provide more accurate accident information prior to a crash as both the traffic environment and the crash process can be observed from the video. In this study, a set of typical AEB test scenarios for ETWs was developed using accident data with video information. Video recordings of 630 car-to-ETW crashes in China from 2010 to 2021 were selected from the VRU Traffic Accident database with Video (VRU-TRAVi). A K-medoids cluster analysis was carried out based on variables including the collision time, visual obstruction, motion of the car and ETW before the collision, relative motion direction between the car and ETW, and the ETW type. The velocity information of cars and ETWs was also accounted for in each clustering scenario. Seven typical pre-crash scenarios were obtained, including five electric-scooter (E-scooter) scenarios (representing two scenarios where the ETWs are approaching the car from the left side, two scenarios where the ETWs are approaching the car in the same direction and another scenario where the ETWs are approaching the car in the opposite direction) and two electric-bike (E-bike) scenarios where the E-bikes are approaching the car in the perpendicular direction. Both E-bike scenarios are consistent with the E-scooter scenario except for the ETW type and velocity range; therefore, by combining the E-bike and E-scooter scenarios, five ETW scenarios were finally recommended as AEB test scenarios. By comparing with typical scenarios extracted based on the China In-Depth Accident Study (CIDAS) data and the China New Car Assessment Program (C-NCAP) test scenarios, the results show that future AEB test scenarios for ETWs should focus on scenarios with visual obstructions and scenarios where either the car or the ETW is turning, with a velocity range of 15-30 km/h for ETWs.

摘要

电动两轮车(ETW)的碰撞安全性一直是中国最重要的安全问题之一,因为它们在交通事故中的比例很高。自动紧急制动(AEB)系统已被证明可以有效减少交通事故中的死亡和受伤人数。提供测试场景是为 ETW 建立一套 AEB 测试程序所需的基本任务之一。与传统的深入事故数据相比,带有视频记录的事故数据在事故发生前提供了更准确的事故信息,因为可以从视频中观察到交通环境和碰撞过程。在这项研究中,使用带有视频信息的事故数据为 ETW 开发了一套典型的 AEB 测试场景。从 2010 年至 2021 年,从 VRU 交通事故数据库(带视频)(VRU-TRAVi)中选择了 630 起中国汽车与 ETW 碰撞的视频记录。基于包括碰撞时间、视觉障碍、碰撞前汽车和 ETW 的运动、汽车和 ETW 之间的相对运动方向以及 ETW 类型等变量,进行了 K-medoids 聚类分析。在每个聚类场景中还考虑了汽车和 ETW 的速度信息。获得了七个典型的预碰撞场景,包括五个电动滑板车(E-scooter)场景(代表两个 ETW 从左侧接近汽车的场景、两个 ETW 以相同方向接近汽车的场景和另一个 ETW 相反方向接近汽车的场景)和两个电动自行车(E-bike)场景,其中 E-bikes 垂直接近汽车。除了 ETW 类型和速度范围外,两个 E-bike 场景与 E-scooter 场景一致;因此,通过将 E-bike 和 E-scooter 场景相结合,最终推荐了五个 ETW 场景作为 AEB 测试场景。通过与基于中国深入事故研究(CIDAS)数据和中国新车评估计划(C-NCAP)测试场景提取的典型场景进行比较,结果表明,未来 ETW 的 AEB 测试场景应侧重于有视觉障碍的场景和汽车或 ETW 转弯的场景,ETW 的速度范围为 15-30km/h。

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