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

Jorge D. Cardenas; Omar Contreras-Ponce; Carlos A. Gutierrez; Ruth Aguilar-Ponce; Francisco R. Castillo-Soria; Cesar A. Azurdia-Meza

arXiv · 2309.08737

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

Jorge D. Cardenas·Omar Contreras-Ponce·Carlos A. Gutierrez·Ruth Aguilar-Ponce·Francisco R. Castillo-Soria·Cesar A. Azurdia-Meza

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Abstract

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.

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Jorge D. Cardenas, Omar Contreras-Ponce, Carlos A. Gutierrez, Ruth Aguilar-Ponce, Francisco R. Castillo-Soria, Cesar A. Azurdia-Meza. 2023-09-15. Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing. https://arxiv.org/abs/2309.08737

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