  While short range 3D pedestrian detection is sufficient for emergency
breaking, long range detections are required for smooth breaking and gaining
trust in autonomous vehicles. The current state-of-the-art on the KITTI
benchmark performs suboptimal in detecting the position of pedestrians at long
range. Thus, we propose an approach specifically targeting long range 3D
pedestrian detection (LRPD), leveraging the density of RGB and the precision of
LiDAR. Therefore, for proposals, RGB instance segmentation and LiDAR point
based proposal generation are combined, followed by a second stage using both
sensor modalities symmetrically. This leads to a significant improvement in mAP
on long range compared to the current state-of-the art. The evaluation of our
LRPD approach was done on the pedestrians from the KITTI benchmark.
