# Low-cost low-power in-vehicle occupant detection with mm-wave FMCW radar

## TL;DR

This paper presents a low-cost, low-power mm-wave FMCW radar system with a novel Capon filter-based algorithm for in-vehicle occupant detection, achieving high accuracy with minimal features and computational effort.

## Contribution

It introduces a new joint range-azimuth estimation algorithm and demonstrates effective occupant detection using machine learning on a low-cost radar system.

## Key findings

- Achieved 97.8% average accuracy in occupant detection.
- Correctly identified vehicle occupancy with 100% accuracy.
- Validated system performance in real vehicle experiments.

## Abstract

In this paper, we use a low-cost low-power mm-wave frequency modulated continuous wave (FMCW) radar for the in-vehicle occupant detection. We propose an algorithm using Capon filter for the joint range-azimuth estimation. Then, the minimum necessary features are extracted to train machine learning classifiers to have reasonable computational complexity while achieving high accuracy. In addition, experiments were carried out in a minivan to detect occupancy of each row using support vector machine (SVM). Finally, our proposed system achieved 97.8% accuracy on average in finding the defined scenarios. Moreover, the system can correctly identify if the vehicle is occupied or not with 100% accuracy.