论文翻译，传感器融合的汽车应用.doc

Sensor Fusion for Automobile Applications Personnel: Y. Fang, (I. Masaki, B.K.P.Horn) Sponsorship: Intelligent Transportation Research Center at MIT’s MTL Introduction To increase the safety and efficiency for transportation systems, many automobile applications need to detect detail obstacle information. Highway environment interpretation is important in intelligent transportation systems (ITS). It is expect to provide 3D segmentation information for the current road situation, i.e., the X, Y position of objects in images, and the distance Z information. The needs of dynamic scene processing in real time bring high requirements on sensors in intelligent transportation systems. In complicated driving environment, typically a single sensor is not enough to meet all these high requirements because of limitations in reliability, weather and ambient lighting. Radar provides high distance resolution while it is limited in horizontal resolution. Binocular vision system can provide better horizontal resolution, while the miscorrespondence problem makes it hard to detect accurate and robust Z distance information. Furthermore, video cameras could not behave well in bad weather. Instead of developing specialized image radar to meet the high ITS requirements, sensor fusion system is composed of several low cost, low performance sensors, i.e., radar and stereo cameras, which can take advantage of the benefit of both sensors. Typical 2D segmentation algorithms for vision systems are challenged by noisy static background and the variation of object positions and object size, which leads to false segmentation or segmentation errors. Typical tracking algorithms cannot help to remove the errors of initial static segmentation since there are significant changes between successive video frames. In order to provide accurate 3D segmentation information, we should not simply associate distance information for radar and 2D segmentation information from video camera. It is ex