# A Multi-Stage Clustering Framework for Automotive Radar Data

## TL;DR

This paper introduces a novel multi-stage clustering framework for automotive radar data that improves environmental perception by filtering static background and applying a two-step clustering process, optimized for better data merging.

## Contribution

The paper presents a new multi-stage clustering approach with background filtering and parameter optimization, enhancing radar data processing for autonomous driving.

## Key findings

- Filtering static background improves clustering accuracy.
- Two-stage clustering enhances data grouping quality.
- Optimized parameters lead to better merging results.

## Abstract

Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.