On using Machine Learning Algorithms for Motorcycle Collision Detection
This addresses the critical need for rapid and accurate collision detection in motorcycles to reduce severe injuries and fatalities, but appears incremental as it applies existing ML methods to a new domain-specific dataset.
The paper tackled the problem of reliably detecting impending motorcycle collisions to activate passive safety systems, and investigated the applicability of machine learning algorithms by training classification models on simulated accident data and assessing their performance.
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.