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
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.