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 high-end 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 with maximum 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.
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