Identifying Critical Test Scenarios for Lane Keeping Assistance System Using Analytic Hierarchy Process and Hierarchical Clustering

doi:10.1109/TIV.2023.3297592

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Identifying Critical Test Scenarios for Lane Keeping Assistance System Using Analytic Hierarchy Process and Hierarchical Clustering
	Song, Rui 1,2; Li, Xuan 3; Zhao, Xiangmo 1; Liu, Mingyang 4; Zhou, Jianhua 4; Wang, Fei-Yue 5
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-10-01
卷号	8 期号:10 页码:4370-4380
关键词	Analytic hierarchy process Accidents autonomous vehicles critical test scenarios hierarchical clustering lane keeping asistance system scenarios engineering
ISSN号	2379-8858
DOI	10.1109/TIV.2023.3297592
通讯作者	Li, Xuan(lix05@pcl.ac.cn)
英文摘要	Lane keeping assistance system (LKAS) is critical components of autonomous driving technologies, and their reliable operation is crucial to ensure passenger safety and maintain traffic flow. The validation of LKAS's safety and performance requires extensive testing using diverse and representative test scenarios. However, identifying the most critical test scenarios from a large data is a significant challenge. Inspired by Scenarios Engineering, the present study introduces a novel approach that includes being the first to combine analytic hierarchy process (AHP) and hierarchical clustering (HC) to identify critical test scenarios for the comprehensive evaluation of LKAS. The proposed approach first utilizes the AHP to rank the key factors that contribute to the failure of the LKAS. Then, by taking the key factors as clustering parameters, the HC method is employed to cluster LKAS failure scenarios with similar characteristics. The experimental results suggest that our method is capable of effectively clustering 200 LKAS failure scenarios into 9 categories of critical, reasonable, and reliable LKAS test scenarios. Furthermore, these 9 categories of critical test scenarios can provide guidance for the development and testing of autonomous driving and serve as a foundation for establishing a testing and evaluation system for LKAS in China and around the world.
资助项目	National Key Research and Development Program of China[2021YFB2501200] ; National Natural Science Foundation of China[62203250] ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology[YESS20210289] ; China Postdoctoral Science Foundation[2020TQ1057] ; China Postdoctoral Science Foundation[2020M682823]
WOS关键词	DECISION-MAKING ; VERIFICATION ; VALIDATION ; VEHICLES
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001109113000006
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology ; China Postdoctoral Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55146]
专题	多模态人工智能系统全国重点实验室
通讯作者	Li, Xuan
作者单位	1.Changan Univ, Sch Informat Engn, Xian 710064, Peoples R China 2.China Automot Technol Res Ctr Co Ltd, Tianjin 300300, Peoples R China 3.Peng Cheng Lab, Shenzhen 518000, Peoples R China 4.CATARC Automot Test Ctr Guangzhou Co Ltd, Guangzhou 511300, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Song, Rui,Li, Xuan,Zhao, Xiangmo,et al. Identifying Critical Test Scenarios for Lane Keeping Assistance System Using Analytic Hierarchy Process and Hierarchical Clustering[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(10):4370-4380.
APA	Song, Rui,Li, Xuan,Zhao, Xiangmo,Liu, Mingyang,Zhou, Jianhua,&Wang, Fei-Yue.(2023).Identifying Critical Test Scenarios for Lane Keeping Assistance System Using Analytic Hierarchy Process and Hierarchical Clustering.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(10),4370-4380.
MLA	Song, Rui,et al."Identifying Critical Test Scenarios for Lane Keeping Assistance System Using Analytic Hierarchy Process and Hierarchical Clustering".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.10(2023):4370-4380.