# Radar-based Feature Design and Multiclass Classification for Road User Recognition

## TL;DR

This paper presents a radar-based approach for classifying road users using 50 features and advanced classifiers, addressing data imbalance with binarization and a novel probability coupling method, achieving improved accuracy.

## Contribution

It introduces a comprehensive feature set, a binarization technique for data imbalance, and a new probability coupling method for multiclass radar classification.

## Key findings

- Significant accuracy improvements over standard methods.
- Effective handling of class imbalance with binarization.
- Enhanced multiclass classification performance.

## Abstract

The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classification