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Powered Two-Wheelers (PTW) control is more complex than any other motorized vehicle control, in particular during emergency events, such as panic braking or last second swerving. For standard PTW, a common cause of accident in these situations is the loss of stability due to braking maneuvers. It is worth noting that for PTW the loss of stability means a high probability of fall, especially while cornering. Accordingly, the aim of this study is to propose a fall detection algorithm for PTW performing maneuvers leading to potential instability. The algorithm is composed of a number of parameters, named RISKi, able to detect potential fall events, critical for PTW safety. This fall detection methodology was developed to alert an advanced riding assistance system in order to produce proper counteractions against the imminent fall.

In the study of new solutions for motorcycle passive safety, FE models of full-scale crash tests play a strategic role. The most important issue in the development process of FE models is their reliability to reproduce real crash tests. To help the engineering in the validation phase, a sensitivity analysis of a FE model for motorcycle-car crash tests is carried-out. The aim of this study is to investigate the model response subjected to variations of specific input parameters. The DOE is performed generating a list of simulations (each one composed by a unique combination of 8 parameters) through Latin Hypercube Sampling. The outputs monitored are the Head Injury Criterion (HIC) and Neck Injury Criteria (Nij). The analysis of the results is performed using scatter plots and linear regression curves to identify the parameters that have major impact on the outputs and to assess the type of dependency (linear or non-linear).