We introduce a method for the efficient calibration of around-view-monitoring (AVM) cameras. Particularly, we introduce two situations that require calibration because of the characteristics of AVM cameras: a situation wherein cameras are shipped from the manufacturing line and another situation wherein some cameras are distorted during operation and need recalibration. In this study, the calibration method for shipped cameras is defined as the factory mode and that for recalibration is defined as the service mode. In the factory mode, two circular patterns placed at a regular distance are used to ensure the maximum accuracy while requiring minimum calibration. In the service mode, as a recalibration method, we developed a robust method that considers various environments using parallel parking lines that can be easily installed in general service centers. In the factory mode, we confirmed that the AVM-camera-calibration error was within 5.66 cm when the two circular patterns were located at a certain distance within a certain range. However, in the service mode, we achieved camera movement angle error equal to or less than 0.1°using the parking-line-detection result.

Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has limitations in terms of backlight conditions, entering or exiting tunnels, and night driving because of lower image quality. In this paper, we propose an integrated camera with dual light sensor for improving lane detection performance under the worst conditions. We include a new algorithm to enhance image data quality and improve edge detection and lane tracking using illumination information. We evaluate the tests under various conditions on a real road. These tests are performed on 728 km of road (under various external situations and lane types) for false alarm rates. The experimental results show that the system is promising in terms of reliability, enhancement, and improvements.

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The proposed method extracts lines from images using a line filter and a Hough transform, and it uses a convolutional neural network to robustly extract parking lines from the environment. In addition, a parking path consisting of curved and straight sections is created and used to control the vehicle. Perpendicular, angle, and parallel parking paths can be created; however, parking control is applied according to the shape of each parking slot. The results of our experiments confirm that the proposed method has an average offset of 10.3 cm and an average heading angle error of 0.94°.