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A practical and low cost Blind Spot Monitoring system is proposed. By using a single camera, the range and azimuth position of a vehicle in a blind spot are measured. The algorithm is based on the proposed RWA (Range Window Algorithm). The camera is installed on the door mirror and monitoring the side and rear of the host vehicle. The algorithm processes the image and identifies range and azimuth angle of the vehicle in the adjacent lane. This algorithm is applied to real situations. The 388 images including several kinds of vehicles are analyzed. The detection rate is 86% and the range accuracy is 1.6[m]. The maximum detection range is about 30[m].

For the autonomous driving and the ADAS, we have been developing the new vision system. It focuses on detecting the small object at far ranges and keeps the vehicle running smoothly by avoiding the object in advance. This system is based on the high-resolution monocular camera with narrow FOV and the core algorithms for object-detection and lane-detection. Since we have already developed the prototype system, here we have applied it to a field test. The test results are: for traffic cones and arbitrary objects at 50 - 150 [m], the detection rate is 95.6% for n=90, the ghost rate is 0% for n=53, and the range accuracy is 7.1 [m] for n=72. To attain this performance, we also have developed the support algorithms/methods for deleting a ghost object caused by a puddle, determining the side line for an object with obscure boundary and estimating the elevation angle.

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).

As one of the advanced sensing technologies of Autonomous driving, we have started developing the new vision system. It focuses on detecting the small object at far ranges. It makes it possible to detour a vehicle along the traffic cones (small object). This system is based on the high-resolution mono-camera with narrow FOV and the algorithms for object-detection/ranging and lane-detection. A prototype is created and evaluated. It could detect the cones and estimate their ranges up to 150 [m]. The range accuracy is 5.3 [m]. Plural cones at different ranges can be measure at one time. The lane detection could reach 150 [m].