Steering torque constitutes the primary motorcycle control input for the lateral dynamics; consequently, estimating it is important. Conventionally, this is done with complete motorcycle models, requiring significant identification effort. The simplified models in the literature only describe the steering torque under specific cases.This work defined a steering assembly model with few parameters to estimate the steering torque analytically for stationary and transient manoeuvres.The model equations followed from existing motorcycle models through simplifying hypotheses; transfer functions describing the roll response and the Lane Change Roll Index (LCRI) were obtained from these equations. Measured steering torque signals from different datasets, including diverse motorcycle classes, were used as the reference for validation.A good agreement resulted between the estimated and measured torques, in the time and speed-acceleration domains and in terms of LCRI. When using the roll as the motorcycle response, manoeuvrability was highest at lower frequencies. The scooter was the most manoeuvrable; the sports and touring motorcycles were the least manoeuvrable at low and high frequencies, respectively. Concerning design parameters, the front-wheel spin inertia and front twist stiffness influenced manoeuvrability the most.The model allows recreating the steering torque signal for new and pre-existing datasets using commonly measured signals; the signal can describe the riding style and the effort required. Few parameters are required, facilitating its use and reducing the computational burden, allowing its use for steering assistance systems.

Tyre-road interaction governs motorcycle dynamics; however, the most widespread tyre model formulations must be characterised through a dedicated test bench on the lab or road, unavailable to many interested subjects. This article proposed a new tyre model formulation, conceived to be characterised through riding data using standard instrumentation. Albeit its coefficients are identified through quasi-static, uncombined slip manoeuvres, the model addresses transient, combined manoeuvres and is adaptive to road friction levels and static weight through statistical relationships from the literature. A pre-existing formulation was improved and expanded. The model’s behaviour in demanding conditions was investigated through a high-fidelity simulation environment, using a Magic Formula tyre model as the reference. Next, the characterisation procedure was carried out using actual riding data. The model’s accuracy is shown by reproducing numerically one of the manoeuvres and through comparison with the results of a bench test. The proposed model could correctly reproduce the primary behaviour of a Magic Formula model, also concerning tyre moments and steering torque. Characterising the tyre model through real riding data proved feasible, and its robust formulation limited the propagation of estimation errors. The proposed tyre model formulation and characterisation procedure should interest, among others, those subjects that lack access to a tyre testing machine.

Human errors are the primary cause of powered two-wheeler crashes worldwide due to the demanding control required and the often ineffective rider-training programs. Literature on rider behaviour is limited, partly due to the lack of standard investigation methodologies. This work investigated the differences in riding style and capability of a diverse set of riders. It explored the impact of familiarisation and riding instruction through objective metrics. Correlation with experience was a particular focus. Seven riders of various experience levels performed trials on an instrumented motorcycle, following three riding instructions: ‘Free Riding’, ‘Handlebar Riding’, and ‘Body Riding’. Objective metrics assessed rider familiarisation, capability and willingness to excite motorcycle dynamics, riding style, and input preference. Results indicated that riders asymptotically converged to their motorcycle dynamics intensity level after a specific distance; both intensity and distance were positively correlated with experience. Experienced riders achieved higher longitudinal acceleration and utilised combined dynamics to a higher degree. The negative longitudinal jerk during braking varied greatly among riders and correlated with experience. A clustering approach identified two prominent trial groups concerning the motorcycle response intensity. Higher diversity emerged in the inputs, leading to five clusters with distinct riding style meanings. Instructions influenced behaviour, particularly regarding input usage. The unsupervised approach and metrics proposed should make rider behaviour research more straightforward and objective. It could be applied to naturalistic riding sessions for more conclusive evidence of inter-driver differences. The diversity that emerged concerning the command inputs used warrants a revision of training practices to promote riding safety.

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.

The mortality of road accidents quadruples when, in addition to motorists, a motorcyclist is also involved. The higher mortality is influenced by the greater exposure of the rider to injuries during the impact. This problem can be mitigated by reducing, even slightly, the impact’s speed. Bench tests carried out by previous studies showed a considerable influence of the inflation pressure on the properties of motorcycle tires. It could therefore have significant effects on the deceleration achievable under braking. This study aimed to evaluate the effect of inadequate tire inflation pressure (excessive or insufficient) on the average deceleration achievable during a hard braking manoeuvre performed only with the rear wheel and with ABS intervention. Experimental tests were carried out on a Piaggio Beverly S 300 in a controlled road environment closed to traffic. Different inflation pressures of the rear braking tire were tested during multiple runs. The data were acquired through a smartphone used as an IMU. At the nominal pressure of 2.4 bar the measured average deceleration was 3.69 m/s 2 (SD = 0.12 m/s 2 ). When increasing the pressure to 3.0 bar and decreasing it to 1.8 bar the average deceleration was reduced respectively to 3.67 m/s 2 (-0.8%, SD = 0.10 m/s 2 ) and 3.59 m/s 2 (-3.0%, SD = 0.16 m/s 2 ). The reduction in achievable deceleration was partly mitigated by the reduced load transfer on the rear tire, which partially compensated for the reduction of the braking force coefficient. The results showed that a moderate inflation pressure variation on the rear braking tire modestly influences the achievable average deceleration.

Braking assistance systems are already contributing to improving motorcyclists' safety, however, research on emergency systems acting on the steering is lacking. These systems, already available for passenger cars, could prevent or mitigate motorcycle crashes in which safety functions based only on braking are ineffective. The first research question was to quantify the safety impact of diverse emergency assistance systems acting on the steering of a motorcycle. For the most promising system, the second research question was to assess the feasibility of its intervention using a real motorcycle. Three emergency steering assistance systems were defined in terms of Functionality, Purpose, and Applicability: Motorcycle Curve Assist (MCA), Motorcycle Stabilisation (MS), and Motorcycle Autonomous Emergency Steering (MAES). Experts evaluated each system's applicability and effectiveness based on the specific crash configuration (using Definitions for Classifying Accidents - DCA), the Knowledge-Based system of Motorcycle Safety (KBMS), and the In-Depth Crash Reconstruction (IDCR). An experimental campaign was conducted with an instrumented motorcycle to assess the rider's reaction to external steering input. A surrogate method for an active steering assistance system imparted external steering torques in correspondence with a lane change to analyse the effect of the steering inputs on motorcycle dynamics and rider controllability. MAES globally got the best score for each assessment method. MS received better evaluations than MCA in two out of three methods. The union of the three systems covered a sizeable fraction of the crashes considered (maximum score in 22.8% of the cases). An estimation of the injury potential mitigation, based on injury risk functions for motorcyclists, was made for the most promising system (MAES). The field test data and video footage showed no instability or loss of control, despite the high intensity (>20Nm) of the external steering input. The rider interviews confirmed that the external action was intense but manageable. For the first time, this study presents an exploratory assessment of the applicability, benefits, and feasibility of motorcycle safety functions acting on the steering. MAES, in particular, was found applicable to a relevant share of crashes involving motorcycles. Remarkably, applying an external action to produce a lateral avoidance manoeuvre proved feasible in a real-world test setting.

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.

Although diverse tyre model formulations exist in the 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 a 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.