Core Concepts
Investigating the applicability of machine learning algorithms for motorcycle collision detection.
Abstract
The study explores using machine learning algorithms to detect impending collisions in motorcycles. It highlights the need for passive safety systems due to the high risk of injury and fatality in motorcycle accidents. Various simulations are conducted to collect data for training classification models, which are then evaluated based on performance criteria. The research aims to improve safety measures for motorcycles by reliably detecting collisions within milliseconds.
Stats
"Worldwide, about 375,000 drivers and passengers of two- or three-wheeled vehicles die each year."
"In Germany alone, 302 motorcyclists were killed and 5,230 seriously injured in 13,702 motorcycle accidents with personal injury in 2021."
"The Honda Goldwing GL1800 heavy touring motorcycle is the only one available on the market that detects an impact with an accident opponent."
Quotes
"No false detection should be raised when not at risk for accidents."
"Detection delay must be sufficiently short for airbag deployment before rider impacts the motorcycle."