An intelligent driver warning system for vehicle collision avoidance

This paper describes a basic architecture of an intelligent driver warning system which embodies an adaptive driver model for indirect collision avoidance. In this study, the driver modelling objective is focused only on longitudinal car-following, and the model inputs are chosen to be the past history of throttle angle, controlled vehicles's speed, range and range rate to the front vehicle whereas the model output is chosen to be the current throttle angle. An artificial neural network called CMAC and a conventional linear model are independently applied to model the real driver data taken from test track and motorway environments. The CMAC model is chosen because of its nonlinear modeling capability, on-line learning convergence and minimum learning interference characteristics, whereas the linear model is chosen as a control benchmark to examine the nonlinear characteristics of the driver's behaviour. The modeling capabilities are then evaluated based on one-step ahead prediction error performances over the training and testing sets, learning curves and correlation based model validation techniques. Modeling results suggest that the past history of the throttle dynamics plays a critical role in reducing the deviation of the error correction, which in turn suggest that the throttle dynamics is generally slow for road driving. Also, the time scale dependency of the model on the driver's behaviour varies significantly from the test track to motorway environment. In the driver modeling experiment, the time scale was chosen such that the deviation of the error correlation was minimised. The test track results suggest that the chosen inputs are indeed relevant variables for modeling the driver's behaviour. Unlike that of the CLM, the degree of error deviation was found to be acceptable for the test track scenario, implying a significant nonlinear coupling of the throttle output with the speed, range and range rate data. Whereas for the motorway data, the modeling performance for both models is comparable, and the time scale of the driver model is approximately three times longer than that used in the test track data