Particle Filter Based Pedestrian Tracking Using Point Cloud toward Obstacle Avoidance Control^*

Accurately estimating the position and speed of pedestrians is crucial for autonomous vehicles to operate safely in congested environments. LiDAR technology is highly accurate in acquiring the distance to objects, but pedestrian point clouds may be occluded in practical situations. To address this issue, we propose a method that represents pedestrian existence as a probability distribution using kernel density estimation (KDE), accommodating occlusion with a Particle Filter (PF).