Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory

arXiv:2405.10575v1 Announce Type: new
Abstract: Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and training datasets to convert LiDAR scans into occupancy grid maps yield very low quality, and subsequently present a novel approach using evidence theory …

abstract acquired arxiv automated cs.cv data driving evidence geometry images knowledge lidar map maps modern prediction scanners theory training training data type