Development of a motorcycle rider model with focus on head and neck biofidelity, recurring to line element muscle models and feedback control

Despite continuing improvements in vehicle safety, motorcyclist casualties are estimated between 13% and 17% of road fatalities. Looking at the last two ESV conferences for a tentative measure of the research effort that is geared towards motorcycle safety, oral/written papers referring to two-wheelers averaged 6%/3% of each group. This tendency is also identifiable in the clearly lagging development of experimental techniques and computational models for the study of crash scenarios involving PTWs. This status quo prompts further developments of PTW-specific design tools to stem from existing occupant (and pedestrian) tools, rather than already available motorcycle-specific solutions. This paper aims at filling some of that gap by proposing developments in computational models for motorcyclists alongside real-world trials. The paper concludes that a MADYMO human body model, equipped with PID-controlled neck muscles, reasonably maintains its biofidelic erect posture in sample scenarios, under the assumption that riders attempt to maintain their head upright. Preliminary results yield activation levels of up to 50 and 55% during severe (± 1,7G and 0,8G) longitudinal and lateral loading scenarios, respectively. Preliminary volunteer trials (N=8) were conducted to provide initial validation in the event of braking. Although not yet complete, the analysis suggests that the resulting head kinematics for an average aware volunteer is compatible with the simulated response. This development focuses R&D efforts on preventing injuries to the head-neck-complex, the body’s most vulnerable region, by providing biofidelic postures and reactions to developers of personal protective equipment and advanced occupant/rider restraint systems. It also allows the evaluation of a motorcycle active safety system’s impact on human response, which directly influences the consequences of the potential subsequent pre-crash or crash event. Finally, it represents a first step towards fully active human models, which will provide life-like pre-crash behaviour to e.g. OEMs, equipment and barrier manufacturers, and policy makers