Although diverse tyre model formulations exist in literature, they are suited for characterisation using a dedicated test bench, preventing parameters' estimation in driving conditions.This study defined a novel motorcycle tyre model, characterisable through driving manoeuvres using simple instrumentation consisting in an inertial measurement unit, steering position sensor and wheel speed sensors. Acquired signals were used to estimate instantaneous tyre forces, moments, slip angle and other properties, which were employed to calculate tyre model's parameters.Tyre model development and validation were performed in simulation environment: we used a Magic Formula tyre-equipped motorcycle model to perform a set of manoeuvres, which were employed to characterise the proposed tyre model. Lastly, a set of quasi-static manoeuvres was conducted using the same motorcycle model equipped with the two tyre models, and results were compared.Comparison results showed a close reproduction of real tyre forces, moments and slips by the proposed tyre model for quasi-static manoeuvres, accurately reproducing motorcycle dynamics. Therefore, steering torque was correctly predicted for different lateral acceleration values.These results show that the proposed tyre model can be characterised, for both longitudinal and lateral dynamics, using this limited set of manoeuvres and simple instrumentation; the correctly predicted steering torque could allow the use of this tyre model for handling description.
Motorcycle simulators are employed for rider training, studying human–machine interaction, and developing assistance systems. However, existing simulators are either too simple and, therefore, unsuitable or significantly complex, with higher hardware costs and familiarisation times. This study aimed to use a tuned single-track car model as the basis of a motorcycle simulator, leading to considerable software simplification while preserving its fidelity. In particular, the approach defined a conversion between motorcycle steering torque and car steering angle. It modified the parameters of the latter to reproduce the response of various motorcycle models in quasi-static and transient conditions for different speeds and radii of curvature. A robust manoeuvrability index was chosen. For the car, it was possible to calculate it from its parameters analytically. Next, the car yaw inertia was tuned to obtain a motorcycle-like steering response. Finally, the calibrated car model was implemented into a low-complexity motorcycle simulator for objective validation. It was verified that an understeering single-track model with high yaw inertia has amplitude and phase responses analogous to a motorcycle. The experimental results of the simulator test confirmed these findings for a diverse set of manoeuvres, validating the method. This straightforward approach allows the development of low-complexity simulators with good steering fidelity, using an objective procedure to reproduce the behaviour of a chosen motorcycle class. In addition, the low computational cost of the model makes it a potential candidate for use in assistance systems.
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.
When riding a motorcycle, the applied steering torque and the lateral rider body movement influence its trajectory. Reproducing the effect of body and motorcycle roll on a simulator would improve its realism. However, this goal is still challenging, especially on low-complexity simulators such as the MOVING simulator of the University of Florence. In order to achieve this result, this study defined a control logic to introduce steering effects linked with the mockup passive inclination operated by the rider. The logic computed a roll-related steering input consisting of equivalent steering torque. This contribution was added to that the rider exerted on the handlebar. A validation test with participants revealed improvements over the baseline, roll-insensitive approach, especially in stationary and medium-high speed manoeuvres. Interestingly, the riders unconsciously tended to use larger mockup roll angles as the roll sensitivity increased. The logic was optimised for stationary manoeuvres; however, the subjective feedback provided by the participants indicated a good level of riding realism also during transients. This simple and effective logic opens the way for new methodologies to improve the realism of motorcycle simulators, encouraging their development and use.
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