# Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation

## TL;DR

This paper introduces a data-driven deep learning approach for occupancy grid mapping from sparse radar clusters, formulated as a semantic segmentation task, outperforming classical methods in real-world autonomous driving data.

## Contribution

It presents the first learning-based inverse sensor model for radar occupancy grid mapping using semantic segmentation, leveraging lidar data for ground truth.

## Key findings

- Learned occupancy net outperforms classical filtering methods
- Effective handling of radar data sparsity and noise
- Demonstrated on NuScenes dataset with significant improvements

## Abstract

Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches.In this work, we propose learning the inverse sensor model used for occupancy grid mapping from clustered radar data. This is done in a data driven approach that leverages computer vision techniques. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.