Recognition and Comparison of Driving Styles of Heavy-Duty Truck Drivers Under Different Scenarios

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Recognition and Comparison of Driving Styles of Heavy-Duty Truck Drivers Under Different Scenarios

Yu, Linghua / Ma, Yongfeng / Chen, Shuyan / Yao, Hong / Zhou, Muxiong

With the acceleration of urbanization, the demand for heavy-duty trucks has increased and transportation safety and management issues are facing large challenges. The heavy-duty truck driver’s behavior is characterized by his or her driving style and plays an important role in driving safety. Consequently, this paper proposes a novel framework to classify driving styles of heavy-duty trucks and make comparision under different scenarios. On rural road and urban road, 11 heavy-duty truck drivers were chosen to conduct experiments under no load or full load. VBOX device was applied to collect data including speed, acceleration and location information. K-means clustering was used to divide driving style into three categories including aggressive, normal and calm. The results show that load and road environment have a great influence on the driving style of heavy-duty truck drivers. It is worth noting that heavy-duty truck drivers are more aggressive with full load than no load when on urban roads. The empirical results demonstrate that the proposed method has efficiency in recognizing the driving style and reveal the variations of the driving style of heavy-duty truck drivers under different scenarios. Moreover, it is meaningful and practical to analyze the driving style in improving road construction, traffic safety and reducing energy consumption.

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Dokumentinformationen

Titel :

Recognition and Comparison of Driving Styles of Heavy-Duty Truck Drivers Under Different Scenarios

Weitere Titelangaben:

Lect. Notes Electrical Eng.

Beteiligte:

Wang, Wuhong (Herausgeber:in) / Wu, Jianping (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Li, Ruimin (Herausgeber:in) / Zhang, Haodong (Herausgeber:in) / Yu, Linghua (Autor:in) / Ma, Yongfeng (Autor:in) / Chen, Shuyan (Autor:in) / Yao, Hong (Autor:in) / Zhou, Muxiong (Autor:in)

Kongress:

International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021

Erschienen in:

Green Transportation and Low Carbon Mobility Safety ; Kapitel : 52 ; 717-732

Lecture Notes in Electrical Engineering ; 944

Erscheinungsdatum :

2022-10-28

Format / Umfang :

16 pages

ISBN :

978-981-19-5615-7 , 978-981-19-5614-0

ISSN :