A design of three DSRC-based rear-end collision warning models.

Road traffic crashes result in a significant number of fatalities and economic losses worldwide. Rear-end collisions, caused by insufficient distance between vehicles, are a major contributor to these accidents. To address this issue, the use of Dedicated Short-Range Communication (DSRC) technology has been developed prominence in last decade. However, uncertainties associated with DSRC-based systems, such as GPS positioning errors and transmission delays, pose challenges to the development of robust collision warning models. In this paper, a design of three DSRC-based rear-end collision warning models to mitigate the impact of uncertainties. The models include the vehicle kinetic model (VK), the maximum compensation vehicle kinetic model (MC VK), and the artificial neural network model (NN). The VK model shows both a high possibility of false warning and missing. The MC VK model shows a 0% possibility in missing warning, which can be determined as the early warning boundary. The NN model shows low probability in both false and missing warning, and 80% correct warning makes it a good candidate for emergency warning boundary. [ABSTRACT FROM AUTHOR]