A learning-based evaluation for lane departure warning system considering driving characteristics

Abstract

Misunderstanding the driver behavior in the next short time is the primary reason of the false warning for the lane departure warning system. This paper proposes a learning-based evaluation to predict whether the driver notices the deviation of the vehicle and takes corrective actions. First, statistical Gaussian model and K-means clustering method are utilized to classify driving style of drivers and determine warning areas based on key driving parameters extracted in driving scenarios. Then, according to the vehicle trajectory in the warning area and the time of lane crossing (TLC) value of the two warning area boundaries, an advanced horizontal dual-area warning (HDAW) model that is trained by bi-direction long short-term memory (BiLSTM) originated from recurrent neural network (RNN) is applied to predict the lane departure and corrective behavior of driver. The personalized warning strategy is finally developed by considering driver characteristics, which allows the warning system to adapt to different driving styles of drivers. Natural driving data from 57 drivers in the experimental driving simulator are collected to train personalized prediction and verify proposed evaluation method. The recent directional sequence of piecewise lateral slopes (DSPLS) and traditional TLC are also researched and compared. Experimental results show that the proposed approach has as low as false alarm rate of 3.97% and can improve prediction accuracy approximately 41.39% over DSPLS method.