• The Journal of the Convergence on Culture Technology
• /
• v.4 no.3
• /
• pp.221-226
• /
• 2018

Special-purposed vehicles are customized according to the user's requirements. When these vehicles are out of oder, they are costly and time consuming to repair. In order to solve these problems, we want to remotely check whether the vehicle is abnormal and remotely identify the fault area, thereby shortening the repair cost and the repair period. In this study, the faulty part of the electric control part is automatically identified, and it is immediately grasped through the user's mobile phone application and an instant fault code is notified to the car manufacturer for quick and smooth fault repair. In order to realize this, we want to build a system that uses the technology of IoT to determine the fault area according to the items required in the field of the special purpose vehicle and notify the manufacturer of the fault on its own.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.7
• /
• pp.498-506
• /
• 2003

A computer vision system applied to an intelligent safety vehicle has been required to be worked on a small sized real time special purposed hardware not on a general purposed computer. In addition, the system should have a high reliability even under the adverse road traffic environment. This paper presents a design and an implementation of an onboard hardware system taking into account for high speed image processing to analyze a road traffic scene. The system is mainly composed of two parts: an early processing module of FPGA and a postprocessing module of DSP. The early processing module is designed to extract several image primitives such as the intensity of a gray level image and edge attributes in a real-time Especially, the module is optimized for the Sobel edge operation. The postprocessing module of DSP utilizes the image features from the early processing module for making image understanding or image analysis of a road traffic scene. The performance of the proposed system is evaluated by an experiment of a lane-related information extraction. The experiment shows the successful results of image processing speed of twenty-five frames of 320$\times$240 pixels per second.