On using Machine Learning Algorithms for Motorcycle Collision Detection
Philipp Rodegast Steffen Maier Jonas Kneifl Jörg Fehr
Globally, motorcycles attract vast and varied users. However, since the rate
of severe injury and fatality in motorcycle accidents far exceeds passenger car
accidents, efforts have been directed toward 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.