abstract

• This paper studies the performance of an automatic head-on vehicle collision warning system based on a decentralized sensing approach using radio frequency (RF) signals. To identify a vehicle moving toward another, a communication system is used in reception mode, and a continuous wave (CW) RF signal transmitted by a third vehicle driving behind as a probe signal. The gathered signal was classified using a long short-term memory neural network. A data set consisting of CW RF signals was collected in a series of experiments in a highway scenario. The signals were processed to find Doppler signatures of approaching vehicles. This information is used to detect events of interest. Our results demonstrate the system%27s feasibility, obtaining a precision of 98.6%25 in a multi-class classification assessment. © 2024 IEEE.

authors

• Aguilar Ponce Ruth Mariela
• Azurdia-Meza C.A.
• Castillo Soria Francisco Rubén
• Gutiérrez Díaz De León Carlos Adrián

publication date

• 2024-01-01

published in

• IEEE Vehicular Technology Conference  Journal

keywords

• Deep Learning; Doppler Signatures; Radio Sensing; Vehicular Collision Automatic vehicle identification; Image analysis; Image thinning; Magnetic levitation vehicles; Radio frequency identification (RFID); Vehicle actuated signals; Collision warning system; Continuous Wave; Decentralised; Deep learning; Doppler signatures; Head-on collision; Radio sensing; Radiofrequency signals; Vehicular collisions; Wave radio; Highway accidents

Digital Object Identifier (DOI)

• 10.1109/VTC2024-Fall63153.2024.10758009

PubMed ID

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