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

Abstract

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.

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

“…The rider's control strategies were decoupled into two classes based on the type of manoeuvre primitive: stationary, and dynamic, to estimate a personal rider skill score while riding on a winding road. Moreover, recently, in [8], unsupervised learning methods were used for the global analysis of the trajectory of a PTW during bend-taking. The same authors in [16] focused on the curve entry analysis by developing an unsupervised framework.…”

“…The data used in this paper come from a cornering experiment using a heavily instrumented motorcycle on the La Ferte Gaucher track [8]. The experiment aimed to understand how motorcyclists enter a turn, control their path, and exit using a group of experienced and novice riders.…”

“…The instrumented motorcycle was shown and described in detail in Diop et al [8], which was relative to a different test campaign. Only a subset of those measuring instruments was used for this article:…”

“…comparing segments length and order for the same path) and regarding specific phases of the manoeuvre of interest (e.g. detecting the manoeuvre start to analyse the earliest phase of corner entry, as was manually done in Diop et al [8]). Doing this would also facilitate comparing different trials for the same rider to scrutinise average tendencies and behaviour variability when given the same or different instructions.…”

Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data

“…Modern phones support GNSS positioning and are frequently used to navigate. GNSS has, for example, already been used to cluster bend-taking practices of motorcyclists [20]. However, urban canyons or forest areas restrict the quality of positioning and the method provides mainly information on the roads used and the speed of travel.…”