Radar-based Dynamic Occupancy Grid Mapping and Object Detection

TL;DR

This paper develops a radar-based dynamic occupancy grid mapping method that fuses multiple radar sensors and applies object tracking and clustering to enhance environment modeling for automated driving, evaluated with real-world urban data.

Contribution

It introduces a novel radar-only dynamic occupancy grid mapping approach with sensor fusion, object tracking, and high-level clustering, filling a gap in existing research.

Findings

Radar-based mapping outperforms lidar in certain urban scenarios.

The method effectively estimates dynamic environment features from radar data.

Quantitative evaluation shows improved accuracy over static models.

Abstract

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 low-level 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.