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Open AccessArticle

Powered Two-Wheeler Riding Profile Clustering for an In-Depth Study of Bend-Taking Practices

1
TS2-SIMU&MOTO, Université Gustave Eiffel, IFSTTAR, F-77447 Marne-la-Vallée, France
2
COSYS-GRETTIA, Université Gustave Eiffel, IFSTTAR, F-77447 Marne-la-Vallée, France
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6696; https://doi.org/10.3390/s20226696
Received: 17 October 2020 / Revised: 12 November 2020 / Accepted: 20 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue Wearable Sensor for Activity Analysis and Context Recognition)
The understanding of rider/vehicle interaction modalities remains an issue, specifically in the case of bend-taking. This difficulty results both from the lack of adequate instrumentation to conduct this type of study and from the variety of practices of this population of road users. Riders have numerous explanations of strategies for controlling their motorcycles when taking bends. The objective of this paper is to develop a data-driven methodology in order to identify typical riding behaviors in bends by using clustering methods. The real dataset used for the experiments is collected within the VIROLO++ collaborative project to improve the knowledge of actual PTW riding practices, especially during bend taking, by collecting real data on this riding situation, including data on PTW dynamics (velocity, normal acceleration, and jerk), position on the road (road curvature), and handlebar actions (handlebar steering angle). A detailed analysis of the results is provided for both the Anderson–Darling test and clustering steps. Moreover, the clustering results are compared with the subjective data of subjects to highlight and contextualize typical riding tendencies. Finally, we perform an in-depth analysis of the bend-taking practices of one subject to highlight the differences between different methods of controlling the motorcycle (steering handlebar vs. rider’s lean) using the rider action measurements made by pressure sensors. View Full-Text
Keywords: powered two-wheeler; riding profiles; riding activity analysis; data mining; wearable sensors powered two-wheeler; riding profiles; riding activity analysis; data mining; wearable sensors
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MDPI and ACS Style

Diop, M.; Boubezoul, A.; Oukhellou, L.; Espié, S. Powered Two-Wheeler Riding Profile Clustering for an In-Depth Study of Bend-Taking Practices. Sensors 2020, 20, 6696. https://doi.org/10.3390/s20226696

AMA Style

Diop M, Boubezoul A, Oukhellou L, Espié S. Powered Two-Wheeler Riding Profile Clustering for an In-Depth Study of Bend-Taking Practices. Sensors. 2020; 20(22):6696. https://doi.org/10.3390/s20226696

Chicago/Turabian Style

Diop, Mohamed; Boubezoul, Abderrahmane; Oukhellou, Latifa; Espié, Stéphane. 2020. "Powered Two-Wheeler Riding Profile Clustering for an In-Depth Study of Bend-Taking Practices" Sensors 20, no. 22: 6696. https://doi.org/10.3390/s20226696

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