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A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shadows are removed by applying a regional dividing method and road region analysis, respectively. Next, the change of lane orientation is monitored in order to define an adaptive line segment separating the region into near and far fields. In the near field, a 1D Hough transform is used to approximate a pair of lane boundaries. Subsequently, river flow method is applied to obtain lane curvature in the far field. Once the lane boundaries are detected, a B-spline mathematical model is updated using a Kalman filter to continuously track the road edges. Simulation results show that the proposed lane detection and tracking method has good performance with low complexity.

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Traffic sign recognition system subsystem in advanced driver assistance syst assisting a driver to detect a critical drivi subsequently making an immediate decision architecture neural network is popular becaus various kind of scenarios, even those which during training. Therefore, a deep architectur is implemented to perform traffic sign classific improve the traffic sign recognition rate. A c for a deep and shallow architecture neural netw in this paper. Deep and shallow architecture refer to convolutional neural network (CNN) function neural network (RBFNN) respe simulation result, two types of training m compared i.e. incremental training and Experimental results show that incrementa trains faster than batch training mode. The pe convolutional neural network is evaluated wi traffic sign database and achieves 99% of the r Keywords-Advance driver assistance syst recognition; Radial basis function neural netwo neural network.

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