2020/08/09 by Christopher Diehl, Eduard Feicho, Alexander Schwambach +3 · 19 citations
Computer Science · Engineering · Psychology · #Autonomous Vehicle Technology and Safety #Cluster analysis #Grid #Human-Automation Interaction and Safety #Object (grammar) #Occupancy #Occupancy grid mapping #Position (finance) #Radar #Sensor fusion #Tracking (education) #Vehicle emissions and performance #cs.CV
paper · pdf · doi:10.1109/itsc45102.2020.9294626
Accepted to be published as part of the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, September 20-23, 2020
arxiv created 2020/08/09 · openalex created_date 2020/08/13 · openalex publication_date 2020/09/20 · arxiv updated 2021/01/12 · openalex updated_date 2026/08/05
Environment modeling utilizing sensor data fusion and object tracking is crucial for safe automated driving. In recent years, the classical occupancy grid map approach, which assumes a static environment, has been extended to dynamic occupancy grid maps, which maintain the possibility of a lowlevel data fusion while also estimating the position and velocity distribution of the dynamic local environment. This paper presents the further development of a previous approach. To the best of the author's knowledge, there is no publication about dynamic occupancy grid mapping with subsequent analysis based only on radar data. Therefore in this work, the data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied. Subsequently, the clustering of dynamic areas provides high-level object information. For comparison, also a lidar-based method is developed. The approach is evaluated qualitatively and quantitatively with real-world data from a moving vehicle in urban environments. The evaluation illustrates the advantages of the radar-based dynamic occupancy grid map, considering different comparison metrics.