A practical methodology using in-depth crash data to support the assessment of new motorcycle safety technologies

Current knowledge on safety technologies developed for passenger cars represents great potential for translatable solutions that may also reduce the number and the severity of casualties among motorcyclists. However, the translation of a safety system conceived for a four-wheeled vehicle to a motorcycle is not straightforward due to the different characteristics in the vehicle dynamics and in common real world crash scenarios. In this paper, we present a methodology to exploit in-depth motorcycle crash data for the purposes of a subsequent assessment of the potential benefits of a promising safety technology for motorcycles: autonomous emergency braking (AEB). The in-depth crash data used in this study involved motorcyclists who were seriously injured following a crash on a public road within 150 km of Melbourne, Victoria. From the subset of cases available for this activity, a set of 20 multi-vehicle crashes in which AEB was considered as “possibly applicable” were identified using a dedicated rating algorithm. For each selected case, the trajectories of the host motorcycle and the other vehicle prior to the crash were estimated using the available in-depth data and reconstructed via 2-dimensional simulations. Finally a panel of investigators reviewed each case until agreement was reached on the accuracy of the reconstruction. In further steps of this research, AEB will also be modelled in the numerical environment. Simulations with and without assistance of AEB will be run to predict the effects that this safety technology may have produced in the reconstructed cases.