Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions
Purpose – An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of g...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-02-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2021-0008/full/pdf?title=using-naturalistic-driving-data-to-identify-driving-style-based-on-longitudinal-driving-operation-conditions |
| _version_ | 1797333617809031168 |
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| author | Nengchao Lyu Yugang Wang Chaozhong Wu Lingfeng Peng Alieu Freddie Thomas |
| author_facet | Nengchao Lyu Yugang Wang Chaozhong Wu Lingfeng Peng Alieu Freddie Thomas |
| author_sort | Nengchao Lyu |
| collection | DOAJ |
| description | Purpose – An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS). Design/methodology/approach – Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data. Findings – The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine. Originality/value – The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS. |
| first_indexed | 2024-03-08T08:07:32Z |
| format | Article |
| id | doaj.art-73e3da26ded8418bbc2bf92014bae562 |
| institution | Directory Open Access Journal |
| issn | 2399-9802 |
| language | English |
| last_indexed | 2024-03-08T08:07:32Z |
| publishDate | 2022-02-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj.art-73e3da26ded8418bbc2bf92014bae5622024-02-02T10:15:19ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022022-02-0151173510.1108/JICV-07-2021-0008676617Using naturalistic driving data to identify driving style based on longitudinal driving operation conditionsNengchao Lyu0Yugang Wang1Chaozhong Wu2Lingfeng Peng3Alieu Freddie Thomas4Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, ChinaPurpose – An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS). Design/methodology/approach – Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data. Findings – The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine. Originality/value – The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2021-0008/full/pdf?title=using-naturalistic-driving-data-to-identify-driving-style-based-on-longitudinal-driving-operation-conditionsmachine learningadvanced driver assistant systemsdriver behaviors and assistancesensor data processing |
| spellingShingle | Nengchao Lyu Yugang Wang Chaozhong Wu Lingfeng Peng Alieu Freddie Thomas Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions Journal of Intelligent and Connected Vehicles machine learning advanced driver assistant systems driver behaviors and assistance sensor data processing |
| title | Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions |
| title_full | Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions |
| title_fullStr | Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions |
| title_full_unstemmed | Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions |
| title_short | Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions |
| title_sort | using naturalistic driving data to identify driving style based on longitudinal driving operation conditions |
| topic | machine learning advanced driver assistant systems driver behaviors and assistance sensor data processing |
| url | https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2021-0008/full/pdf?title=using-naturalistic-driving-data-to-identify-driving-style-based-on-longitudinal-driving-operation-conditions |
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