# Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing

arXiv: https://arxiv.org/abs/2309.08737 · HTML: https://mlsanity.com/paper/2309.08737/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Forward Collision Vehicular Radar with IEEE 802.11: Feasibility Demonstration through Measurements](https://mlsanity.com/paper/1702.03351/) (2017)
- [Vehicular Behavior-Aware Beamforming Design for Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2302.14763/) (2023)

## Abstract (arXiv)

This paper presents the idea of an automatic forward-collision warning system based on a decentralized radio sensing (RS) approach. In this framework, a vehicle in receiving mode employs a continuous waveform (CW) transmitted by a second vehicle as a probe signal to detect oncoming vehicles and warn the driver of a potential forward collision. Such a CW can easily be incorporated as a pilot signal within the data frame of current multicarrier vehicular communication systems. Detection of oncoming vehicles is performed by a deep learning (DL) module that analyzes the features of the Doppler signature imprinted on the CW probe signal by a rapidly approaching vehicle. This decentralized CW RS approach was assessed experimentally using data collected by a series of field trials conducted in a two-lanes high-speed highway. Detection performance was evaluated for two different DL models: a long short-term memory network and a convolutional neural network. The obtained results demonstrate the feasibility of the envisioned forward-collision warning system based on the fusion of DL and decentralized CW RS.