Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity

This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behav...
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Gespeichert in:

Autor*in:	Xian-Sheng Li [verfasserIn] Xiao-Tong Cui [verfasserIn] Yuan-Yuan Ren [verfasserIn] Xue-Lian Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Driving behavior unsupervised driving style analysis dynamic decision-making process driving maneuver curve clustering

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 48160-48178
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:48160-48178

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3171347

Katalog-ID:	DOAJ028059522

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allfields	10.1109/ACCESS.2022.3171347 doi (DE-627)DOAJ028059522 (DE-599)DOAJ8ee137e856134defa790ced7514f0f89 DE-627 ger DE-627 rakwb eng TK1-9971 Xian-Sheng Li verfasserin aut Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles. Driving behavior unsupervised driving style analysis dynamic decision-making process driving maneuver curve clustering Electrical engineering. Electronics. Nuclear engineering Xiao-Tong Cui verfasserin aut Yuan-Yuan Ren verfasserin aut Xue-Lian Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 48160-48178 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:48160-48178 https://doi.org/10.1109/ACCESS.2022.3171347 kostenfrei https://doaj.org/article/8ee137e856134defa790ced7514f0f89 kostenfrei https://ieeexplore.ieee.org/document/9765515/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_370 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 10 2022 48160-48178
spelling	10.1109/ACCESS.2022.3171347 doi (DE-627)DOAJ028059522 (DE-599)DOAJ8ee137e856134defa790ced7514f0f89 DE-627 ger DE-627 rakwb eng TK1-9971 Xian-Sheng Li verfasserin aut Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles. Driving behavior unsupervised driving style analysis dynamic decision-making process driving maneuver curve clustering Electrical engineering. Electronics. Nuclear engineering Xiao-Tong Cui verfasserin aut Yuan-Yuan Ren verfasserin aut Xue-Lian Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 48160-48178 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:48160-48178 https://doi.org/10.1109/ACCESS.2022.3171347 kostenfrei https://doaj.org/article/8ee137e856134defa790ced7514f0f89 kostenfrei https://ieeexplore.ieee.org/document/9765515/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_370 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 10 2022 48160-48178
allfields_unstemmed	10.1109/ACCESS.2022.3171347 doi (DE-627)DOAJ028059522 (DE-599)DOAJ8ee137e856134defa790ced7514f0f89 DE-627 ger DE-627 rakwb eng TK1-9971 Xian-Sheng Li verfasserin aut Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles. Driving behavior unsupervised driving style analysis dynamic decision-making process driving maneuver curve clustering Electrical engineering. Electronics. Nuclear engineering Xiao-Tong Cui verfasserin aut Yuan-Yuan Ren verfasserin aut Xue-Lian Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 48160-48178 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:48160-48178 https://doi.org/10.1109/ACCESS.2022.3171347 kostenfrei https://doaj.org/article/8ee137e856134defa790ced7514f0f89 kostenfrei https://ieeexplore.ieee.org/document/9765515/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_370 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 10 2022 48160-48178
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allfieldsSound	10.1109/ACCESS.2022.3171347 doi (DE-627)DOAJ028059522 (DE-599)DOAJ8ee137e856134defa790ced7514f0f89 DE-627 ger DE-627 rakwb eng TK1-9971 Xian-Sheng Li verfasserin aut Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles. Driving behavior unsupervised driving style analysis dynamic decision-making process driving maneuver curve clustering Electrical engineering. Electronics. Nuclear engineering Xiao-Tong Cui verfasserin aut Yuan-Yuan Ren verfasserin aut Xue-Lian Zheng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 48160-48178 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:48160-48178 https://doi.org/10.1109/ACCESS.2022.3171347 kostenfrei https://doaj.org/article/8ee137e856134defa790ced7514f0f89 kostenfrei https://ieeexplore.ieee.org/document/9765515/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_370 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 10 2022 48160-48178
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callnumber-first	T - Technology
author	Xian-Sheng Li
spellingShingle	Xian-Sheng Li misc TK1-9971 misc Driving behavior misc unsupervised driving style analysis misc dynamic decision-making process misc driving maneuver misc curve clustering misc Electrical engineering. Electronics. Nuclear engineering Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity
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topic_title	TK1-9971 Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity Driving behavior unsupervised driving style analysis dynamic decision-making process driving maneuver curve clustering
topic	misc TK1-9971 misc Driving behavior misc unsupervised driving style analysis misc dynamic decision-making process misc driving maneuver misc curve clustering misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Driving behavior misc unsupervised driving style analysis misc dynamic decision-making process misc driving maneuver misc curve clustering misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Driving behavior misc unsupervised driving style analysis misc dynamic decision-making process misc driving maneuver misc curve clustering misc Electrical engineering. Electronics. Nuclear engineering
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title	Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity
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title_full	Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity
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title_sort	unsupervised driving style analysis based on driving maneuver intensity
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title_auth	Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity
abstract	This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles.
abstractGer	This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles.
abstract_unstemmed	This paper proposes a novel unsupervised clustering framework to identify driving style not in terms of the discrete features of driving behavior data, but rather the time-varying patterns of driving maneuver intensity. This framework can describe the dynamic decision-making process of driving behavior and the continuity of driving data, and driving maneuver intensity is the basic of this paper. Therefore, detection, feature analysis and clustered on intensity of driving maneuvers are carried out using a threshold-based approach, hierarchical feature extraction, and k-means clustering. Then, to analyze fine-grained driving style, dynamic time windows are determined according to road alignment. In dynamic time windows, this paper constructs time-varying patterns based on driving maneuver intensity, which consider the intensity and frequency of driving behavior and preserve the time-varying characteristics of time-series data. However, not all dynamic time windows are equal in maneuvers’ duration and number, which means the time-varying patterns of driving maneuver intensity are curves with various lengths. So that, for clustering time-varying patterns, this paper proposes a novel curve clustering algorithm named Similarity-Based Clustering with Dynamic Time Warping (SBC-DTW) that can cluster curves with various lengths. The empirical results based on real driving data demonstrate that the proposed framework can classify driving style more accurately than the classical method. Moreover, according to this framework, we can have an in-depth understanding of dynamic driving behavior and the composition of drivers’ long-term driving styles.
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title_short	Unsupervised Driving Style Analysis Based on Driving Maneuver Intensity
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author2	Xiao-Tong Cui Yuan-Yuan Ren Xue-Lian Zheng
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