A Deep Neural Model for Pedestrians Detection with Danger Estimation

Personal mobility vehicles (PMV) with the autonomous driving system is required for supporting movement-limited persons. To realize their safe movement, researchers extract pedestrian features from images without considering pedestrian characteristics. However, before these extractions, it is also important to estimate which person should be paid attention and assigning priority order by the degree of danger for PMV's collision avoidance. In this paper, we propose a deep neural model for pedestrian detection with danger estimation. Finally, we show some experimental results and discuss parameters that are important for pedestrian danger estimation for each experimental scenario.