摘要
智能汽车测试是其技术开发与应用中必不可少的环节,封闭场景下测试目标物准确反映真实道路环境下交通对象特性是保障测评结果可信的关键,而道路弱势群体服饰色彩是相应测试目标设计的关键参数,也是智能车测评相关标准中要求的一个主要指标。为此,通过对中国某省份2018~2020年间重大交通安全事故案例的分析和筛查,得出178例弱势道路使用者群体伤亡人员样本,首先提取样本服饰颜色,然后选取适当的色彩空间,将色彩数据从RGB(Red-Green-Blue)空间转换至LUV(Lightness-Chroma)空间。以转换结果作为聚类参数,采用K-means聚类算法,获取受害者样本基于季节、出行方式等不同因素下的服饰代表颜色。区别现阶段欧洲标准中目标物黑色上衣/蓝色长裤的搭配组合,黑色上衣/黑色长裤作用于符合中国国情的自动驾驶场景中测试目标物的服饰颜色更具代表性。鉴于中国新车评价规程(China-New Car Assessment Programme,C-NCAP)选取行人目标物与自行车骑行者目标物,将目标物服饰改为黑色上衣/黑色长裤组合,以测试目标物与测试车辆位置分别构建相对横向及纵向运动的多个场景,在对应场景下检测汽车前端结构位置25%、50%及75%处与目标物碰撞情况,以评价配有自动紧急制动系统(Automatic Emergency Braking System,AEB)的智能汽车对测试目标物的响应能力。试验测试结果表明:全部测试场景下,测试车辆能够成功识别目标物并可主动制动,该测试验证了黑色上衣/黑色长裤组合在现行检测标准下的可行性与有效性。该研究可为智能汽车测试领域提供客观的数据支撑,完善交通行业相关标准和法规,并推动智能汽车测试技术的发展。
The testing of intelligent vehicles is paramount to their technological development and application.Whether the test surrogates can accurately reflect the characteristics of the traffic objects in the actual road environment of in-field testing is key to ensuring the credibility of the evaluation results.Meanwhile,the clothing color of vulnerable groups on the road is a key parameter for designing the vulnerable group surrogates as well as a main indicator in the relevant standards for intelligent vehicle evaluation.By analyzing major traffic accident in one representative province in China from 2018 to 2020,a sample of 178 victim cases is obtained.Firstly,the clothing color of the samples is extracted.Subsequently,the appropriate color space is converted from RGB(Red-Green-Blue)space to LUV(Brightness,Chroma)space.Using the conversion result as the clustering parameter,the K-means clustering algorithm is applied to obtain the representative clothing color based on different factors such as age,season,and travel mode.Different from the clothing color combination of a black tops/blue pants of the surrogates in the current European standard,a black tops and black pants combination is more representative of the scenarios in China.To conform to China-New Car Assessment Programme(C-NCAP)regulations,multiple Near and Far scenarios with black tops and black pants of pedestrian surrogate and bicyclist surrogate are constructed respectively.The collision points between the testing vehicle and surrogate at 25%,50%and 75%of the bumper of the testing vehicle in the corresponding scenarios are analyzed to evaluate the response ability of the intelligent vehicle equipped with an automatic emergency braking system.The results show that in all scenarios,the testing vehicle can successfully identify the target and brake actively.These tests verified the feasibility and effectiveness of the black tops/black pants combination under the current testing standards.The results provide sufficient data support for intelligent vehicle tes
作者
韩玲
朱长盛
迟瑞丰
方若愚
张晖
刘国鹏
伊强
HAN Ling;ZHU Chang-sheng;CHI Rui-feng;FANG Ruo-yu;ZHANG Hui;LIU Guo-peng;YI Qiang(School of Mechanical and Electrical Engineering,Changchun University of Technology,Changchun 130012,Jilin,China;Indiana University-Purdue University Indianapolis,Indianapolis IN46074,Indiana,USA;Hebei Pride Automotive Technology Co.Ltd.,Shijiazhuang 050010,Hebei,China)
出处
《中国公路学报》
EI
CAS
CSCD
北大核心
2023年第1期240-252,共13页
China Journal of Highway and Transport
基金
吉林省自然科学基金项目(20220101236JC)。
关键词
汽车工程
服饰色彩
K-means聚类分析
测试目标物
测试场景
自动紧急制动系统
智能汽车测试
automotive engineering
clothing color
K-means clustering analysis
test object
test scenario
automatic emergency braking system(AEB)
intelligent vehicle testing
