Obstacle detection test in real-word traffic contexts for the purposes of motorcycle autonomous emergency braking (MAEB)
This addresses safety for motorcycle riders by potentially mitigating crash severity, but it appears incremental as it focuses on testing feasibility rather than introducing a new solution.
The study tackled the problem of accurate obstacle detection for Motorcycle Autonomous Emergency Braking (MAEB) in real-world traffic by conducting tests in a MAEB-sensitive crash scenario, but no concrete results or numbers were provided.
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 track vehicle has not been confirmed yet. To address this issue, our study presents obstacle detection tests in a real-world MAEB-sensitive crash scenario.