Data-Driven Methodology for the Investigation of Riding Dynamics: A Motorcycle Case Study

• Published in: IEEE transactions on intelligent transportation systems 2023-09, Vol.24 (9), p.1-14
• Main Authors: Bartolozzi, Mirco, Boubezoul, Abderrahmane, Bouaziz, Samir, Savino, Giovanni, Espie, Stephane
• Format: Article
• Language: English
• Subjects: Algorithms; bend negotiation; Bends; Clustering; Clustering algorithms; Dynamics; Engineering Sciences; Instruments; Labeling; Maneuvers; Manuals; motorcycle lateral dynamics; Motorcycles; Other; Powered two-wheelers; Segmentation; Segments; Signal and Image processing; Time series; Time series analysis; unsupervised clustering and segmentation; Vehicle dynamics
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3271790

Powered-Two-Wheelers (PTW) riders' fatalities are prevalent on bends outside built-up areas due to the complexity and instability of their vehicles: countermeasures require a better understanding of the rider-PTW interaction. Analysing riding data is effective but becomes challenging when using extensive datasets; segmenting the riding data would help identify events of interest, isolate specific manoeuvres and describe the riding session. Manual segmentation would be time-consuming and subjective; automation would be beneficial. This work proposed an automatic, unsupervised tool for segmenting and clustering signals acquired during a riding session for studying motorcycle lateral dynamics in-depth. The method only requires measuring the motorcycle roll angle. An expert rider completed a closed route using an instrumented motorcycle; the algorithm divided the time series into segments categorised into clusters relative to specific riding conditions. Analysing the segmented trial revealed the effectiveness and usefulness of the approach. Then, a corner entry manoeuvre was investigated in-depth to observe each segment's properties. The method associated each riding primitive to a cluster and described each manoeuvre through the segments' succession. The clusters were unambiguous and easy to interpret thanks to their dynamics-based nature and minimal overlap. The algorithm identified the differences between the three corner entry manoeuvres in the trial. The segmentation simplified the in-depth corner entry analysis and allowed early detection of the manoeuvre start. The proposed tool can aid research on motorcycle dynamics, PTW-rider interaction, and riding preferences in bends. The segmented time series could be employed for rider training and pre-crash fall dynamics reconstruction.