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RESEARCH ON A DSRC-BASED REAR-END COLLISION WARNING MODEL
ABSTRACT:
Dedicated short-range communication (DSRC) is an emerging technology that allows vehicles to
communicate with each other. The rear-end collision warning system based on DSRC has its
unique advantages. However, there are problems (e.g., high rates of false alarms and missing
alarms in emergency warnings) in the system due to uncertain measurement errors. In this paper,
we propose to address the problems by establishing a robust rear-end collision warning model
without using expensive highend devices. Simulations have shown that high rates (up to 56%) of
missing alarms occur in the vehicle kinematics (VK) model, as well as false alarms (most of
which exceed 70%) in the VK model withmaximum compensation (VK-MC). Pertaining to these
rates, a novel model based on the neural network (NN) approach is implemented. Through
training and validation, the NN model is able to provide emergency warnings with an improved
performance of false alarm probability under 20% and the missing alarm probability under 10%
for all test cases.