Driving Behavior Detection And Evaluation Based On Multi-source Vehicle Driving Data

It is of great significance to study the mechanism of driving behavior,or to construct a people-oriented automobile safety system for reducing traffic accidents and people’s loss of life and property.The identification and detection of driving behavior is the basis of driving behavior related research.In this paper,a method of comprehensive recognition and evaluation of driving behavior based on multi-source vehicle driving data is proposed,which identifies driver’s lane-changing behavior by video data of vehicle front-facing camera.At the same time,driver’s over-speeding,accelerated and decelerated behavior is acquired by vehicle GPS trajectory data.Driver’s abnormal behavior identification results are used to evaluate driver’s driving level.The research methods proposed in this paper can be used to construct driving behavior portraits and vehicle risk portraits in the future.Driving behavior portraits can not only help drivers improve driving behavior,but also help bus operators to identify good and bad drivers,and achieve fine operation.Vehicle risk portraits can help vehicle insurance companies optimize their business processes.The main contents of this paper are as follows:(1)An improved algorithm for lane detection based on machine visionThe improved lane detection algorithm proposed in this paper can effectively solve the problems of fewer adaptive scenes,low detection accuracy and poor robustness that exist in existed algorithms.In the phase of image preprocessing,in order to eliminate the irrelevant information in the image and enhance the detectability of the relevant information,so as to improve the reliability of object extraction and detection,the traditional image preprocessing methods are improved in this paper: an improved median filtering method and an adaptive threshold edge detection algorithm are proposed.The experimental results show that the preprocessing algorithm in this paper can effectively reduce image noise and improve the effect of subsequent image processing.In the stage of lane recognition and fitting,a lane recognition algorithm based on feature matching is proposed.The algorithm uses vanishing point feature,lane spacing feature and lane width feature to extract lane line.Extrapolation method is used to compensate lane position information of completely abnormal missing frame image to ensure the stability of recognition algorithm.Parallel quadrilateral method is proposed to obtain lane line shape and color information.Verification experiments show that the proposed lane recognition algorithm based on feature matching can be applied to a variety of complex driving scenarios with high recognition accuracy.(2)Recognition and evaluation of driving behavior based on multi-source dataIt is difficult to evaluate driver’s driving level comprehensively when using singlesource driving data.Vehicle-mounted camera data can easily be used to identify the driver’s lane-changing behavior,but lack of information about speeding and acceleration and deceleration;while GPS trajectory data can effectively identify the driver’s speeding,acceleration and deceleration behavior,but the accuracy of lanechanging behavior recognition is low.This paper combines the vehicle camera image data and GPS trajectory data to obtain the driver’s comprehensive lane-changing,overspeeding,acceleration and deceleration behavior,and establishes a comprehensive evaluation method of driver’s driving behavior.