Vision-based Driving Behavior Modeling

Transportation safety problems have become more and more obvious with rapid growth of vehicle population. Driving behavior modeling is one of the key technologies of autonomous driving and safety vehicle assistant driving, which has great theoretical significance and applied value in reducing accidents and improving traffic safety. In this dissertation, the exploratory research work has been done regarding driving behavior modeling based on vision, which include3D traffic scene reconstruction, traffic scene understanding, dangerous traffic events detecting, and driving behavior modeling.The main contributions of this dissertation are summarized as follows:(1) The3D reconstruction method of traffic scene based on image sequence is proposed. The EM algorithm is adopted to estimate static feature points and the fundamental matrix. In step E, static feature points of traffic scene are detected by using epipolar constraint and fundamental matrix. In step M,8-points algorithm is applied to compute the fundamental matrix based on the static feature points.This method also estimates Projection Matrix by using singular value decomposition and the restriction of absolute conic. The reconstruction method of traffic scene's moving points based on PCA is proposed. This method determines the trajectory basis by using PCA, and the trajectory of scene's moving points can be seen as the linear combination of trajectory basis. The reconstruction of traffic scene is achieved by calculating the3-D trajectory using least squares method. This method omits the process of projective reconstruction and reduces the computational complexity distinctly. Experimental results prove the effectiveness of this method.(2) An approach for traffic scene understanding is proposed based on covariance descriptor. In order to overcome the drawback of segmenting and recognizing the traffic scene based on single feature, this method adopts the movement structure features, texture and color features in traffic sense, and uses covariance descriptors to integrate multi-feature for eliminating feature redundancy and effects on image segmentation causing by the numerical disparity of different features. The multiclass LogitBoost classifier is used for image segmentation to improve the accuracy of segmentation. Experimental results show that this method can effectively improve the effect of traffic scene segmentation and recognition.(3)The method is proposed to detect dangerous traffic events based on visual attention models. The Hemispherical sparse sampling method has been adopted to improve the detecting speed; The Bayesian probability models and Gaussian kernel function has been used to do nonparametric saliency measure of video in order to analyze visual attention; The calculation method base on multi-scale saliency map has been used to improve the detection accuracy. The experimental results show that this method can effectively detect traffic danger.(4) The method of driving behavior modeling based on Bayesian model is proposed. This method can predict the appropriate driving behavior based on the traffic scene and the car's own speed, location information. Besides, the model parameters are estimated by adopting the sparse Bayesian learning method. This model achieves the prediction of several driving behaviors such as going straight, changing lanes, accelerating, decelerating and so on. The experimental results show that our method has a good performance on driving behavior predicting.(5) The method to model driving behaviors is put forward based on fuzzy rules under a dangerous traffic environment. The fuzzy rules of seven driving behavior is established according to the driver's experience. It gives a mixed membership function by combining Gaussian function with Sigmoid function and an estimation of the model parameters of fuzzy rules by adopting a C-means clustering method and gradient descent. The experimental results show that this method can be very good for driver's behavior description and can be used in all kinds of driving behavior decisions when it is dangerous.