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

Purpose - Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiaohan Li [verfasserIn] Wenshuo Wang [verfasserIn] Zhang Zhang [verfasserIn] Matthias Rötting [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Feature selection Machine learning Effect size Lane-change maneuvers Naturalistic driving data

Übergeordnetes Werk:	In: Journal of Intelligent and Connected Vehicles - Tsinghua University Press, 2019, 1(2019), 3, Seite 85-98
Übergeordnetes Werk:	volume:1 ; year:2019 ; number:3 ; pages:85-98

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1108/JICV-09-2018-0010

Katalog-ID:	DOAJ040564851

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520			\|a Purpose - Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach - In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings - It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value - In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
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spelling	10.1108/JICV-09-2018-0010 doi (DE-627)DOAJ040564851 (DE-599)DOAJ48e137f08d874e6abda374678fe490b5 DE-627 ger DE-627 rakwb eng TL1-4050 Xiaohan Li verfasserin aut Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose - Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach - In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings - It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value - In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios. Feature selection Machine learning Effect size Lane-change maneuvers Naturalistic driving data Motor vehicles. Aeronautics. Astronautics Wenshuo Wang verfasserin aut Zhang Zhang verfasserin aut Matthias Rötting verfasserin aut In Journal of Intelligent and Connected Vehicles Tsinghua University Press, 2019 1(2019), 3, Seite 85-98 (DE-627)1066588228 (DE-600)2964162-7 23999802 nnns volume:1 year:2019 number:3 pages:85-98 https://doi.org/10.1108/JICV-09-2018-0010 kostenfrei https://doaj.org/article/48e137f08d874e6abda374678fe490b5 kostenfrei https://www.emeraldinsight.com/doi/pdfplus/10.1108/JICV-09-2018-0010 kostenfrei https://doaj.org/toc/2399-9802 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2019 3 85-98
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topic_facet	Feature selection Machine learning Effect size Lane-change maneuvers Naturalistic driving data Motor vehicles. Aeronautics. Astronautics
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authorswithroles_txt_mv	Xiaohan Li @@aut@@ Wenshuo Wang @@aut@@ Zhang Zhang @@aut@@ Matthias Rötting @@aut@@
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Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value - In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. 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callnumber-first	T - Technology
author	Xiaohan Li
spellingShingle	Xiaohan Li misc TL1-4050 misc Feature selection misc Machine learning misc Effect size misc Lane-change maneuvers misc Naturalistic driving data misc Motor vehicles. Aeronautics. Astronautics Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
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topic_title	TL1-4050 Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data Feature selection Machine learning Effect size Lane-change maneuvers Naturalistic driving data
topic	misc TL1-4050 misc Feature selection misc Machine learning misc Effect size misc Lane-change maneuvers misc Naturalistic driving data misc Motor vehicles. Aeronautics. Astronautics
topic_unstemmed	misc TL1-4050 misc Feature selection misc Machine learning misc Effect size misc Lane-change maneuvers misc Naturalistic driving data misc Motor vehicles. Aeronautics. Astronautics
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title	Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
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title_full	Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
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title_sort	effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
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title_auth	Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
abstract	Purpose - Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach - In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings - It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value - In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
abstractGer	Purpose - Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach - In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings - It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value - In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
abstract_unstemmed	Purpose - Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach - In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings - It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value - In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
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title_short	Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
url	https://doi.org/10.1108/JICV-09-2018-0010 https://doaj.org/article/48e137f08d874e6abda374678fe490b5 https://www.emeraldinsight.com/doi/pdfplus/10.1108/JICV-09-2018-0010 https://doaj.org/toc/2399-9802
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Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data

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