Naoto Ishikawa

Automatic Recognition of Road Signs

The increase in traffic accidents is becoming a serious social problem with the recent rapid traffic increase. In many cases, the driver’s carelessness is the primary factor of traffic accidents, and the driver assistance system is demanded for supporting driver’s safety. In this research, we propose the new method of automatic detection and recognition of road signs by image processing. The purpose of this research is to prevent accidents caused by driver’s carelessness, and call attention to a driver when the driver violates traffic a regulation. In this research, high accuracy and the efficient sign detecting method are realized by removing unnecessary information except for a road sign from an image, and detect a road sign using shape features. At first, the color information that is not used in road signs is removed from an image. Next, edges except for circular and triangle ones are removed to choose sign shape. In the recognition process, normalized cross correlation operation is carried out to the two-dimensional differentiation pattern of a sign, and the accurate and efficient method for detecting the road sign is realized. Moreover, the real-time operation in a software base was realized by holding down calculation cost, maintaining highly precise sign detection and recognition. Specifically, it becomes specifically possible to process by 0.1 sec(s)/frame using a general-purpose PC (CPU: Pentium4 1.7GHz). As a result of in-vehicle experimentation, our system could process on real time and has confirmed that detection and recognition of a sign could be performed correctly.

3D image location surveillance system for the automotive rear-view

We have developed a new type of rear-view observation system. This system can rapidly detect the 3D position and size of obstacles or gutters behind the car and can warn of the danger of collision by anticipating the path the car will take. The system includes a laser projector, a CCD camera, a frame memory, a data processing unit, a steering angle sensor, and a warning display unit. In this paper, some design techniques required for use in an automotive environment are described. We also report the test results obtained under various cruising conditions.<<ETX>>

Stereo-based collision avoidance system for urban traffic

Numerous car accidents occur on urban road. However, researches done so far on driving assistance are subjecting highways whose environment is relatively simple and easy to handle, and new approach for urban settings is required. Our purpose is to extend its support to the following conditions in city traffic: the presence of obstacles such as pedestrians and telephone poles; the lane mark is not always drawn on a road; drivers may lack the sense of awareness of the lane mark. We propose a collision avoidance system, which can be applied to both highways and urban traffic environment. In our system, stereo cameras are set in front of a vehicle and the captured images are processed through a computer. We create a Projected Disparity Map (PDM) from stereo image pair, which is a disparity histogram taken along ordinate direction of obtained disparity image. When there is an obstacle in front, we can detect it by finding a peak appeared in the PDM. With a speed meter and a steering sensor, the stop distance and the radius of curvature of the self-vehicle are calculated, in order to set the observation-required area, which does not depend on lane marks, within a PDM. A danger level will be computed from the distance and the relative speed to the closest approaching object detected within the observation-required area. The method has been tested in urban traffic scenes and has shown to be effective for judging dangerous situation, and gives proper alarm to a driver.

Development of in-vehicle estimation system of the quality of driving

In this research, we propose the concept of the “QOD (The Quality Of Driving)”. We have already established the method for quantifying the state of driving, and then estimate the “QOD”. To evaluate the “QOD” of the driving state in actual urban road, we adopted three parameters: the degree of the meandering, the stability of the acceleration, and the sufficiency of the distance between own vehicle and the vehicle ahead. In our system, we measure these parameters from the sequence of images taken by the camera placed on the dashboard of our vehicle, and evaluate the “QOD”. Specifically, we acquire the FOE (Focus Of Expansion) by using the optical flow vectors obtained from the sequence of images, and calculate the yaw rate of our vehicle, to get the amount of meandering. Next, velocity of the car is obtained from a velocity sensor and the stability of the acceleration is computed. Finally, we extract the domain of the self-lane in road images through the Hough Transform and then detect the horizontal edges within the domain. The front vehicle can be detected from the optical flow vectors emerging out from these edges. Then we calculate the distance between own vehicle and the vehicle ahead by using a camera parameter. The distance between two vehicles is computed by considering the relationship between the distance and the velocity.

Vehicle front scene watching from the sequence of road images

Our aim is to prevent rear-end collision of the vehicle, and we propose a new method for a front-vehicle observation system using sequence of road images. In this research, we have developed an in-vehicle system that can be applied to a software-based rear-end collision with low computing power. First, we calculate projective transformation (a vehicle’s front image is changed into the images seen from right above) of the vehicle’s front image acquired by a single camera placed on the dashboard of our vehicle. Next, the difference of two projective transformation images is calculated. Finally, we calculate the degree of crashing risks depending on the distance to the front vehicle and the speed of our vehicle, and a warning is given to the driver. This system mounted on a vehicle has been tested on expressways. As a result, the effectiveness of our method has been verified.