On the Use of Monte-Carlo Simulation and Deep Fourier Neural Network in Lane Departure Warning

Abstract

To make improvements on vision-based lane departure warning systems (LDWS), a lane departure prediction (LDP) method based on Monte-Carlo simulation and deep Fourier neural network (DFNN) is proposed. Firstly, a closed-loop driver-vehicle-road (DVR) system model is built up and the parameters of the system, consisting of vehicle states, positioning and road conditions, are initialized by random sampling. After simulating a large number of DVR systems with random parameters, the obtained results are used as samples to train a DFNN which predicts the forthcoming maximum lateral deviation and is optimized by employing deep learning method. Then, a LDP strategy is proposed by combining the DFNN with a driver activity index, which takes driver adaptation into consideration. The experimental evaluation shows that the proposed lane departure warning algorithm can predict the lane departure event in time and reduce the false-warning rate of existing methods in a significant way. More importantly, the proposed technique enhances the system's functions of over-speed warning on curved road and over-steer warning on low-adhesion road.