Abstract:

In order to improve the performance of lane departure warning system, an unintentional lane departure analysis method is proposed. This method combines the driver's operation characteristics, vehicle's motion characteristics and the relationship between the vehicle and the lane. First, the unintentional lane departure is classified into two parts: lane departure by fatigue and lane departure by secondary task. Then, experiments of unintentional lane departure are carried out through the co-simulation platform based on CarSim and LabWIEW. Twelve drivers of different genders, proficiencies and driving behaviors are selected to participate the experiments. The unintentional lane departure parameters are collected and analyzed, including the driver's operation behavior, the motion characteristics of the vehicles and the relative motion position between the vehicle and the lane. Finally, an unintentional lane departure recognition model is constructed based on Gaussian Mixture-Hidden Markov Model (GM-HMM). The recognition results show good performance of the proposed model in online and offline tests.

Key words: vehicle engineering, advanced driver assistant system(ADAS), lane departure warning(LDW), unintentional lane departure, Gaussian mixture-hidden Markov model (GM-HMM)