Search Results

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).

A new method to simulate “the FM radio reception on the road” in an open space is proposed. It can provide reliable and objective evaluation of the radio system including a vehicle. It can reduce the cost and time of the driving test on roads. It is based on two technologies. One is a wave-extraction system of incoming waves on the roads and the other the field reconstruction in an open space. For the extraction, 3-D software based on AIC is developed and can estimate the number of waves and the amplitude, phase and polarization of the waves from the field data. The software showed the extraction accuracy of 90% in simulation. This method was applied to the road test, and it was found that the software could extract the waves. And through several tests in the space, the feasibility of the reconstruction of standing wave was confirmed (Maximum-minimum ratio: 18 dB and 22 dB at FM-band for the horizontal and the vertical polarization).

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.

In the Adaptive Cruise Control (ACC) system, the conventional target tracking method depends on the yaw rate/steering angle information and is effective for the uniform road condition, where the host and target vehicles are on the roads with the same curvature. But it cannot work on non-uniform road conditions where the target vehicle and host vehicle are on the road with different curvature like curve-entry and lane change. Therefore a new method to track a target vehicle in non-uniform conditions has been developed. It is based on the locus on the phase chart between the azimuth angle and relative velocity, which are obtained from the radar and host vehicle information. It can express the path of the target vehicle in non-uniform conditions exactly since the locus can be expressed theoretically. The theory of the method and the simulation results are shown here. The performance for the curve-entry, curve-exit and lane-change is satisfactory.

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.

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].