同济大学学报（自然科学版）2021，Vol.49Issue(z1)：123-131,9.DOI:10.11908/j.issn.0253-374x.22710
基于自然驾驶数据的匝道行驶典型场景聚类分析
Clustering Analysis of Typical Ramp Scenarios Based on Naturalistic Driving Data
摘要
Abstract
Ramp driving poses a big challenge to autonomous vehicles,on which there are potential traffic conflicts between vehicles. Therefore,it is necessary to study ramp scenarios for development and testing. In this paper, typical ramp scenarios are studied based on naturalistic driving data (NDD). First, three major elements are defined to describe the interaction between vehicles on the ramp,including the initial state(S),the driving action (A) and the interaction performance (P). Next, variables to characterize the A and the P are selected to be clustering features,and then 8 kinds of categories are obtained by the K-means clustering method based on the Calinski-Harabasz (CH) index. Then, according to the clustering results, 4 kinds of typical interaction modes are obtained by analyzing the variables above. Afterwards, by analyzing the variables that characterize the S,typical logical scenarios are extracted by the confidence ellipse. Finally,based on the logical scenarios,two concrete scenarios are selected to test and evaluate the autonomous driving system (ADS). The results show that testing with typical ramp scenarios can reveal the social cooperation capabilities of autonomous vehicles. Therefore,it is effective to generate typical ramp scenarios by clustering analysis based on NDD.关键词
自动驾驶汽车/自然驾驶数据/匝道行驶典型场景/聚类分析Key words
autonomous vehicle/naturalistic driving data/typical ramp scenarios/clustering analysis分类
交通工程引用本文复制引用
蒙昊蓝,陈君毅,陈磊,万马,余卓平..基于自然驾驶数据的匝道行驶典型场景聚类分析[J].同济大学学报（自然科学版）,2021,49(z1):123-131,9.基金项目
国家重点研发计划(2018YFB0105101) （2018YFB0105101）
上海汽车工业科技发展基金会项目(2114) （2114）
南昌智能新能源汽车研究院前瞻课题资助项目(TPD-TC202110-05) （TPD-TC202110-05）
