This paper presents a robust lane detection algorithm based on RGB color and shape information during autonomous car control in realtime. For realtime control, our algorithm increases its processing speed by employing minimal elements. Our algorithm extracts yellow and white pixels by computing the average and standard deviation values calculated from specific regions, and constructs elements based on the extracted pixels. By clustering elements, our algorithm finds the yellow center and white stop lanes on the road. Our algorithm is insensitive to the environment change and its processing speed is realtime-executable. Experimental results demonstrate the feasibility of our algorithm.

This paper presents a goal-directed reactive obstacle avoidance method based on lane method. The reactive collision avoidance is necessarily required for a robot to navigate autonomously in dynamic environments. Many methods are suggested to implement this concept and one of them is the lane method. The lane method divides the environment into lanes and then chooses the best lane to follow. The proposed method does not use the discrete lane but chooses a line closest to the original target line without collision when an obstacle is detected, thus it has a merit in the aspect of running time and it is more proper for narrow corridor environment. If an obstacle disturbs the movement of a robot by blocking a target path, a robot generates a temporary target line, which is parallel to an original target line and tangential to an obstacle circle, to avoid a collision with an obstacle and changes to and follows that line until an obstacle is removed. After an obstacle is clear, a robot returns to an original target line and proceeds to the goal point. Obstacle is recognized by laser range finder sensor and represented by a circle. Our method has been implemented and tested in a corridor environment and experimental results show that our method can work reliably.

In urban area, thermal pollution associated with heat island phenomena is generally regarded to make urban life uncomfortable. To overcome this urban thermal pollution problem, urban planning with consideration of urban climate, represented by the concept of urban ventilation lane, is widely practiced in many countries. In this study, the prevailing wind ventilation lane of a local winds in Daegu during the warm climate season was investigated by using surface wind data and RAMS(Reasonal Atmospheric Model System) simulation. The domain of interest is the vicinity of Daegu metropolitan city(about 900) and its horizontal scale is about 30km. The simulations were conducted under the synoptic condition of late spring with the weak gradient wind and mostly clear sky. From the numerical simulations, the following two major conclusions were obtained: (1)The major wind passages of the local circulation wind generated by radiative cooling over the mountains(Mt. Palgong and Mt. Ap) are found. The winds blow down along the valley axis over the eastern part of the Daegu area as a gravity flow during nighttime. (2)After that time, the winds blow toward the western part of Daegu through the city center. As the result, the higher temperature region appears over the western part of Daegu metropolitan area.

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At first, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, if vehicle knows the driving conditions which are lane angle, distance error and real position of other vehicles, we should calculate the reference steering angle.