Yuhan Zhang , Xiaomeng Shi , Yichang Shao , Nirajan Shiwakoti , Jian Zhang , Ziyuan Pu , Zhirui Ye
{"title":"A review of scenario cases for autonomous transportation system: Insights from CAV safety testing and scenario generation","authors":"Yuhan Zhang , Xiaomeng Shi , Yichang Shao , Nirajan Shiwakoti , Jian Zhang , Ziyuan Pu , Zhirui Ye","doi":"10.1016/j.aap.2025.107994","DOIUrl":null,"url":null,"abstract":"<div><div>Ensuring the reliability and trustworthiness of connected and automated vehicle (CAV) technologies is crucial before their widespread implementation. Instead of focusing solely on the automation levels of individual vehicles, it is essential to consider the autonomous operations of the entire autonomous transportation system (ATS) to achieve automated traffic. However, designing and generating scenarios that unify the diverse properties of CAV testing and establish mutual trust among stakeholders pose significant challenges. Previous studies have predominantly focused on the automation levels of CAVs when characterizing scenarios, neglecting the autonomous level of the entire scenario from an ATS perspective. Moreover, there remains research potential in evaluating whether the testing scenario libraries can be effectively integrated into the CAV testing process. In this paper, we propose a grading framework for traffic scenarios based on autonomous levels in the ATS. We also classify and summarize the traffic scenarios used in CAV safety testing. Through a comprehensive literature review, we identify prevailing issues and patterns in scenario design and provide insights and directions for future research in this field.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"215 ","pages":"Article 107994"},"PeriodicalIF":5.7000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accident; analysis and prevention","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001457525000806","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ERGONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Ensuring the reliability and trustworthiness of connected and automated vehicle (CAV) technologies is crucial before their widespread implementation. Instead of focusing solely on the automation levels of individual vehicles, it is essential to consider the autonomous operations of the entire autonomous transportation system (ATS) to achieve automated traffic. However, designing and generating scenarios that unify the diverse properties of CAV testing and establish mutual trust among stakeholders pose significant challenges. Previous studies have predominantly focused on the automation levels of CAVs when characterizing scenarios, neglecting the autonomous level of the entire scenario from an ATS perspective. Moreover, there remains research potential in evaluating whether the testing scenario libraries can be effectively integrated into the CAV testing process. In this paper, we propose a grading framework for traffic scenarios based on autonomous levels in the ATS. We also classify and summarize the traffic scenarios used in CAV safety testing. Through a comprehensive literature review, we identify prevailing issues and patterns in scenario design and provide insights and directions for future research in this field.
期刊介绍:
Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.