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

arXiv: https://arxiv.org/abs/1908.04417 · HTML: https://mlsanity.com/paper/1908.04417/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation](https://mlsanity.com/paper/2207.06379/) (2022)
- [Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study](https://mlsanity.com/paper/2503.05629/) (2025)

## Abstract (arXiv)

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.