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A method, which determines the range and lateral position of a preceding vehicle on the road by a single image, had been proposed. It is based on the Range-Window algorithm (RWA) and Pattern Matching (PM). The RWA estimates the range by using multiple virtual windows of fixed physical size at different distances. The size ratio between the windows and a preceding vehicle determines the best window. The associated range of the window will be the range of the vehicle. The PM complementarily estimates the range by using a template obtained through the RWA. It works especially well when an occlusion occurs due to shadows of road side objects. The range estimation is based on the use of horizontally-modified patterns of the template. Namely this PM can perform the range estimation as well as the object extraction. Here, for the real time operation, the calculation time of this method was evaluated after coding the RWA and PM into C-language (16 bit calculation) from Matlab (64 bit).

An algorithm, which determines the range of a preceding vehicle by a single image, had been proposed. It uses a “Range-Window Algorithm”. Here in order to realize higher robustness and stability, the pattern matching is incorporated into the algorithm. A single camera system using this algorithm has an advantage over the high cost of stereo cameras, millimeter wave radar and non-robust mechanical scanning in some laser radars. And it also provides lateral position of the vehicle. The algorithm uses several portions of a captured image, namely windows. Each window is corresponding to a predetermined range and has the fixed physical width and height. In each window, the size and position of objects in the image are estimated through the ratio between the widths of the objects and the window, and a score is given to each object. The object having the highest score is determined as the best object. The range of the window corresponding to the best object becomes an estimated range.

An algorithm, which determines the range of a vehicle on the road by a single image, is proposed. Since it uses a single camera, there is not a problem like the high cost in the stereo camera, the millimeter wave radar and the non-robust mechanical scanning in the laser radar. And it also gives the lateral position of the vehicle. So far it is difficult to get the range information from a single camera. The algorithm overcomes this drawback by introducing a new concept, “Range Window”. It uses several portions of a captured image, namely windows. Each window is corresponding to a certain pre-determined range and has the fixed physical width and height. In each window, the size and position of an object in the image is estimated through the ratio between the widths of the object and the window and a score is given to the window. The window having the best score is determined as a best window. The range corresponding to the best window becomes an estimated range.

A new practical algorithm is proposed for multiple object detection in automotive FM-CW radars. They are radars for ACC (Adaptive Cruise Control) radar, collision avoidance, pre-crash safety, side-object detection, etc. This algorithm can provide the distance and relative velocity of objects without the ambiguity of distance and relative velocity, an inherent problem of FM-CW. Since it is simple, straight-forward and fast, it is suitable for automotive application, in which the update time is less than 100 [msec]. This algorithm is based on two down chirp frequency sweeps with small slope-difference. Since the difference is small, the correct pairs of beat frequencies are obtained automatically. Because of the down chirps, the polarity of beat frequencies owing to the distance and the doppler becomes the same for an approaching object and then the distance and the velocity are uniquely determined.