Obstacle detection test in real-word traffic contexts for the purposes of motorcycle autonomous emergency braking (MAEB)

Abstract

Research suggests that a Motorcycle Autonomous Emergency Braking system (MAEB) could influence 25% of the crashes involving powered two wheelers (PTWs). By automatically slowing down a host PTW of up to 10 km/h in inevitable collision scenarios, MAEB could potentially mitigate the crash severity for the riders. The feasibility of automatic decelerations of motorcycles was shown via field trials in controlled environment. However, the feasibility of correct MAEB triggering in the real traffic context is still unclear . In particular, MAEB requires an accurate obstacle detection, the feasibility of which from a single tra ck vehicle has not been confirmed yet. To address this issue, our study presents obstacle detection tests in a real-world MAEBsensitive crash scenario.