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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.
RESUMO
[This corrects the article DOI: 10.1016/j.dib.2019.103828.].
RESUMO
In this data article, we will present the data coming from 3D Inertial Measurement Unit (3-accelerometers and 3-gyroscopes sensors) mounted on the motorcycle collected during a motorcycle's falls experiments. Developing a motorcycle's fall events detection algorithms is a very challenging task because the motorcycle falling is multi-factorial and is strongly influenced by many unknown factors. To solve this issue, one solution can be to use a data-set collected during controlled experiments, knowing that the real motorcycle falls cannot be replicated, a stuntman can be chosen to be as close to reality as possible. The experiments have been conducted based on predefined scenarios such as: fall in a curve, fall on a slippery straight road section, fall with leaning of the motorcycle ''intentional manoeuvre'' and fall in a roundabout. These scenarios have been designed based on realistic falls. Other experiments have been conducted under different extreme driving situations. These extreme manoeuvres were carried out on track by professional riders. The purpose of performing these manoeuvres was to obtain a dataset describing the limit handling behaviour.
RESUMO
Instrumented vehicles are key tools for in-depth understanding of drivers' behaviours, thus for the design of scientifically based countermeasures to reduce fatalities and injuries. The instrumentation of Powered Two-Wheelers (PTW) has been less widely implemented that for vehicles, in part due to the technical challenges involved. The last decade has seen the development in Europe of several tools and methodologies to study motorcycle riders' behaviours and motorcycle dynamics for a range of situations, including crash events involving falls. Thanks to these tools, a broad-ranging research programme has been conducted, from the design and tuning of real-time falls detection to the study of riding training systems, as well as studies focusing on naturalistic riding situations such as filtering and line splitting. The methodology designed for the in-depth study of riders' behaviours in naturalistic situations can be based upon the combination of several sources of data such as: PTW sensors, context-based video retrieval system, Global Positioning System (GPS) and verbal data on the riders' decisions making process. The goals of this paper are: (1) to present the methodological tools developed and used by INRETS-MSIS (now Ifsttar-TS2/Simu) in the last decade for the study of riders' behaviours in real-world environment as well as on track for situations up to falls, (2) to illustrate the kind of results that can be gained from the conducted studies, (3) to identify the advantages and limitations of the proposed methodology to conduct large scale naturalistic riding studies, and (4) to highlight how the knowledge gained from this approach will fill many of the knowledge gaps about PTW-riders' behaviours and risk factors.
