This uaver Drouoses an avuroach to extract , . . . .. 3) Extraction of matching points between stereo images 4) Segmentation of obstacles generic obstacles on roads using remapped stereo images to an overhead view. The proposed approach uses the fact that the information of roadsurface on the remapped imane is distorted by an obstacle, and formulates the generic obstacle detection (GOD) problem (IS a dynamic pronramminn (DP), which contributes to search c for 5 ) Depth computation . --. corresponding pea.& on polar histograms constructed by vertical edge components of the remapped stereo images. The corresponding p e a k determine the estimates of obstacles'positions. The approach has features that it is not largely affected by intensity diyerence between apair of stereo images and does not depend on the typical stereo matching, and identifies the obstacle's position quite well.

Multi-sensor fusion is important in the field of autonomous driving. A basic prerequisite for multi-sensor fusion is calibration between sensors. Such calibrations must be accurate and need to be performed online. Traditional calibration methods have strict rules. In contrast, the latest online calibration methods based on convolutional neural networks (CNNs) have gone beyond the limits of the conventional methods. We propose a novel algorithm for online self-calibration between sensors using voxels and three-dimensional (3D) convolution kernels. The proposed approach has the following features: (1) it is intended for calibration between sensors that measure 3D space; (2) the proposed network is capable of end-to-end learning; (3) the input 3D point cloud is converted to voxel information; (4) it uses five networks that process voxel information, and it improves calibration accuracy through iterative refinement of the output of the five networks and temporal filtering. We use the KITTI and Oxford datasets to evaluate the calibration performance of the proposed method. The proposed method achieves a rotation error of less than 0.1° and a translation error of less than 1 cm on both the KITTI and Oxford datasets.

: This paper proposes an algorithm to effectively detect the traffic lights and recognize the traffic signals using a monocular camera mounted on the front windshield glass of a vehicle in day time. The algorithm consists of three main parts. The first part is to generate the candidates of a traffic light. After conversion of RGB color model into HSI and YCbCr color spaces, the regions considered as a traffic light are detected. For these regions, edge processing is applied to extract the borders of the traffic light. The second part is to divide the candidates into traffic lights and non-traffic lights using Haar-like features and Adaboost algorithm. The third part is to recognize the signals of the traffic light using a template matching. Experimental results show that the proposed algorithm successfully detects the traffic lights and recognizes the traffic signals in real time in a variety of environments.