A novel rear-end collision warning system using neural network ensemble

Negligence of a driver or a sudden stop of a forward vehicle can cause rear-end collision. In this paper, we propose a new situation assessment algorithm to determine collision probability to prevent the rear-end collision. The proposed algorithm consists of two phases: coarse assessment and fine assessment. In the coarse assessment, the algorithm selects a target vehicle with the highest possibility of collision by using fuzzy logic. In fine assessment, it determines collision probability based on a statistical approach considering driving maneuvers; it models the driving maneuvers to enable the driver to operate the vehicle in conditions toward the collision and calculates the collision probability as the ratio between the total driving maneuvers and the driving maneuvers in possible collisions. To reduce the simulation time complexity, we adapt a neural network. Since there exist variance of widths for different vehicles, we also apply neural network ensemble to cope with the variance. Numerical evaluation of the proposed method is provided through simulations and practical tests.