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Technical Paper

Radar-based Target Tracking Method: Application to Real Road

Principle of the target tracking method for the Adaptive Cruise Control (ACC) system, which is applicable to non-uniform or transient condition, had been proposed by one of the authors. This method does not need any other information rather than that from the radar and host vehicle. Here the method is modified to meet more complex traffic scenarios and then applied to data measured on real highway. The modified method is based on the phase chart between the lateral component of the relative velocity and azimuth of a preceding vehicle. From the trace on the chart, the behavior of a preceding vehicle is judged and the discrimination between the lane change and curve-entry/exit can be made. The method can deal with the lane-change of a preceding vehicle on the curve as well as on the straight lane. And it is applied to more than 20 data including several road/vehicle conditions: road is straight, or turns right or left; vehicles are motorbikes, sedans and trucks.
Technical Paper

Target Tracking by a Single Camera Based on Range-Window Algorithm and Pattern Matching

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.