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Effects of feature selection on lane-change maneuver recognition:an analysis of naturalistic driving data

Journal of Intelligent and Connected Vehicles 2018年第3期1卷 85-98页

作者： Xiaohan Li Wenshuo Wang Zhang Zhang Matthias Rötting Chair of Human-Machine-Systems Technishe Universität BerlinBerlinGermany Department of Mechanical Engineering Carnegie Mellon UniversityPAUSA

Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature *** using feature could take up large storage and excessive computation time,while insufficient feature selection would cause poor *** high contributive features to classify LC and lane-keep behavior is effective for maneuver *** paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving ***/methodology/approach–In total,1,375 LC cases are *** comprehensively select features,the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule *** the effect size(Cohen’s d)and p-value of every feature are computed to assess their contribution for each ***–It has been found that the common lateral features,*** rate,lateral acceleration and time-to-lane crossing,are not strong features for recognition of LC maneuver as empirical ***,cross-validation tests are conducted to evaluate model performance using metrics of receiver operating *** results show that the selected features can achieve better recognition performance than using all the features without ***/value–In this paper,the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving *** aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

关键词： Feature selection Machine learning Effect size Lane-change maneuvers naturalistic driving data

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A review of traffic behaviour and intelligent driving at roundabouts based on a microscopic perspective

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Transportation Safety and Environment 2024年第3期6卷 1-16页

作者： Haobin Jiang Qingyuan Shen Aoxue Li Chenhui Yin Automotive Engineering Research Institute Jiangsu UniversityZhenjiang 212013JiangsuChina School of Automotive and Traffic Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina

The contradiction between increasing traffic and the relatively poor roundabout infrastructure is getting *** control and optimization of the macroscopic traffic flow needs to be improved to resolve congestion and safety problems at roundabouts and the connected road *** order to better understand the gaps and trends in this field,we have systematically reviewed the main research and developments in traffic phenomena,driving behaviour,autonomous vehicles(AVs),intelligent connected vehicles and real vehicle trajectory data sets at *** study is based on 388 papers about roundabouts,selected through a comprehensive literature *** review demonstrates that based on a microscopic perspective,sensing,prediction,decision-making,planning and control aspects of AVs and intelligent connected vehicles can be designed and optimized to fundamentally and significantly improve traffic capacity and driving safety at ***,the generation mechanism of traffic conflicts among traffic participants at roundabouts is complex,which is a tremendous challenge for the systematic design of ***,based on naturalistic driving data and machine learning theory,it is an important research direction to build driver models by learning and imitating human driver decision-making and driving behaviours.

关键词： autonomous vehicles driving behaviour naturalistic driving data roundabout traffic capacity

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