  Rear-end collision warning system has a great role to enhance the driving
safety. In this system some measures are used to estimate the dangers and the
system warns drivers to be more cautious. The real-time processes should be
executed in such system, to remain enough time and distance to avoid collision
with the front vehicle. To this end, in this paper a new system is developed by
using random forest classifier. To evaluate the performance of the proposed
system, vehicles trajectory data of 100 car's database from Virginia tech
transportation institute are used and the methods are compared based on their
accuracy and their processing time. By using TOPSIS multi-criteria selection
method, we show that the results of the implemented classifier is better than
the results of different classifiers including Bayesian network, naive Bayes,
MLP neural network, support vector machine, nearest neighbor, rule-based
methods and decision tree. The presented experiments reveals that the random
forest is an acceptable algorithm for the proposed driver assistant system with
88.4% accuracy for detecting warning situations and 94.7% for detecting safe
situations.
