LAUSR

A previously developed real-time forward collision warning system (RCWS) using a multi-layer perceptron neural network (MLPNN) with a single hidden layer aims to be implemented with in-vehicle sensor and smartphone under cloud-based communication environment. However, several issues exist concerning the communication delay between the smartphone and the cloud server, especially when uploading massive traffic information to the cloud server simultaneously. In order to mitigate the impact of the delay, this research proposes two modified RCWSs using an advanced feed-forward neural network (F2N2). One of them involves MLPNN with two hidden layers and the other includes radial basis function network. The modified RCWSs are evaluated by the real-time warning accuracy under different market penetration rates (MPRs) and delays. The evaluation shows that the warning performances of each RCWS increase when the MPR increases or the delay decreases overall. In addition, the modified RCWSs outperform the original one in all conditions. Furthermore, the performance gap between the modified RCWSs increases as the MPR decreases and the delay increases. These findings suggest that the advanced F2N2 model can be an effective alternative for uprating the performance of the RCWS, particularly under a large delay with low MPR.