A study on the rear-end collision warning system by considering different perception-reaction time using multi-layer perceptron neural network

A rear-end Collision Warning System (CWS) is applied for mitigating collision risk to the frontal motor vehicle under the traffic conditions. Most of the previous studies have been performed to address the braking behavior related problems based on the deterministic or stochastic parametric methods. However, these algorithms are of doubtful validity in the context of individual driving characteristics such as Perception-Reaction Time (PRT). This paper proposes a framework on Rear-end CWS to take into consideration of PRT effects based on the Artificial Neural Network (ANN). Multi-layer perceptron neural network based rear-end collision warning algorithm (MCWA) is developed and evaluated through a comparison between the conventional algorithms such as Time To Collision (TTC) and Stopping Distance Algorithm (SDA). The comparison study demonstrates that the proposed algorithm outperforms other traditional algorithms for detecting and predicting the rear-end collision risks. The proposed algorithm could be used for rear-end collision warning in car-following case without the influence of different human PRT.