Xihua University, National Experiment Teaching Demonstration Center of Automotive Engineering, 610039, Chengdu, China; Chongqing Jiaotong University, College of Traffic & Transportation, 400074, Chongqing, China; Sichuan Xihua Jiaotong Forensic Science Center, 610039, Chengdu, China.
Xihua University, National Experiment Teaching Demonstration Center of Automotive Engineering, 610039, Chengdu, China.
Accid Anal Prev. 2021 Feb;150:105857. doi: 10.1016/j.aap.2020.105857. Epub 2020 Dec 5.
Road safety remains a challenge with numerous Vulnerable Road Users (VRUs) suffering from injuries and death every year. Pedestrian protection using active safety systems, such as Automated Emergency Braking (AEB), is an effective measure to combat the situation. Furthermore, the perception of precrash scenarios plays an important role in active safety research. It is essential to understand and define precrash scenarios. This study aimed to apply the obtained typical car-to-pedestrian precrash scenarios from Chinese severely injured pedestrian traffic accidents to develop and test active safety systems. The National Automobile Accident In-Depth Investigation System (NAIS) recorded 467 cases from 2011 to 2018 in China, and 12 items were selected from the NAIS database as description variables for the precrash scenario. The items were divided into four categories: car, pedestrian, road, and environment. Group decision theory was applied to evaluate the importance of each variable in its category. A total of 34 basic scenarios were defined and obtained according to the extracted significant variables. These basic scenarios represented diverse fatal scenarios in China which are crucial for autonomous driving. The frequency distribution of the scenarios demonstrated that the top five scenarios covered 85.3 % of the total. Five scenarios were identified to have the common characteristic of cars going straight. Additionally, 13 detailed scenarios were obtained from the five basic scenarios by using cluster and frequency analyses. In contrast to the New Car Assessment Program (NCAP) test scenarios, weather and lighting conditions were considered in these 13 scenarios, and the driving speed before the crash were mostly distributed in the range of 40-80 km/h (20-60 km/h in the NCAP). Meanwhile, both walking and running were commonly recorded for pedestrians to cross the street from the nearside, compared with records of walking only to cross from the nearside in the NCAP. These results contribute to a reference for test scenarios of pedestrian AEB or Forward Collision Warning (FCW) in China.
道路安全仍然是一个挑战,每年都有许多弱势道路使用者(VRU)受伤和死亡。使用主动安全系统(如自动紧急制动(AEB))保护行人是应对这种情况的有效措施。此外,预碰撞场景的感知在主动安全研究中起着重要作用。了解和定义预碰撞场景至关重要。本研究旨在将从中国严重受伤行人交通事故中获得的典型车对行人预碰撞场景应用于开发和测试主动安全系统。国家汽车事故深入调查系统(NAIS)记录了 2011 年至 2018 年中国的 467 起事故,从 NAIS 数据库中选择了 12 项作为预碰撞场景的描述变量。这些项目分为四类:汽车、行人、道路和环境。应用群体决策理论评估每个类别中变量的重要性。根据提取的显著变量,共定义并获得了 34 个基本场景。这些基本场景代表了中国自主驾驶的多样化致命场景,对其至关重要。场景的频率分布表明,前五个场景占总数的 85.3%。五个场景被确定具有汽车直走的共同特征。此外,还通过聚类和频率分析从五个基本场景中获得了 13 个详细场景。与新车评估计划(NCAP)测试场景相比,这些场景考虑了天气和照明条件,并且碰撞前的驾驶速度大多分布在 40-80km/h(NCAP 为 20-60km/h)范围内。同时,与 NCAP 中记录的仅行人从近侧行走相比,记录了更多行人从近侧行走和奔跑以穿过街道。这些结果为中国行人 AEB 或前方碰撞警告(FCW)的测试场景提供了参考。