On using machine learning algorithms for motorcycle collision detection

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds that of passenger car accidents, efforts have been directed towards increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria. The challenge of reliably detecting a motorcycle collision within a short time by means of machine learning classification is investigated. Different machine learning architectures are compared in terms of their practical capability with metrics specifically adapted to the problem. Performance is validated on standardized ISO 13232 accident configurations.