# 2D Car Detection in Radar Data with PointNets

## TL;DR

This paper introduces a novel method using PointNets for 2D object detection in radar data, enabling simultaneous classification and bounding box estimation from sparse radar targets, which is promising for autonomous driving perception.

## Contribution

It presents the first approach combining object classification and amodal bounding box regression in radar data using PointNets, tailored for 2D detection in high-resolution radar sensors.

## Key findings

- High detection accuracy demonstrated on realistic driving data.
- Effective joint classification and bounding box estimation from sparse radar targets.
- Potential for improved perception in autonomous driving systems.

## Abstract

For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.