In Intelligent Transportation Systems, Advanced Vehicle Control and Safety System are one of the most important researches around the world. AVCSS is a technique applied on vehicle and is composed by sensor, computer, communication, and control. In order to keep driver safe, the technique covered Collision Avoidance, Longitudinal Automated Control, Lateral Automated Control, Automated Parking, etc, and Collision Avoidance, Longitudinal Automated Control, Lateral Automated Control are most important. This thesis implemented the Lateral Automated Control by using a CCD camera to extract the road environment. And I presented and analyzed lane detection about structured road and unstructured road. The structured road stands for its obvious lane mark such as general road and freeway; and the unstructured road stands for its unobvious lane mark or without lane mark such as country road and campus road. Because of the characteristic of lane mark, the structured road is easier to detect, and there were less research about unstructured road around the world. So this thesis focused on the unstructured lane detection, and implemented multi-system on DSP (Digital Signal Process). Finally, we applied intelligent control system to vehicle and successfully guided the vehicle in structure and unstructured road.

For an autonomous outdoor mobile robot ability to detect roads existing around is a vital capability. Unstructured roads are among the toughest challenges for a mobile robot both in terms of detection and navigation. Even though mobile robots use various sensors to interact with their environment, being a comparatively low-cost and rich source of information, potential of cameras should be fully utilized. This research aims to systematically investigate the potential use of streaming camera images in detecting unstructured roads. The investigation focused on the use of methods employing Artificial Neural Networks (ANNs). An exhaustive test process is followed where different kernel sizes and feature vectors are varied systematically where trainings are carried out via backpropagation in a feed-forward ANN. The thesis also claims a contribution in the creation of test data where truth images are created almost in realtime by making use of the dexterity of human hands. Various road profiles v ranging from human-made unstructured roads to trails are investigated. Output of ANNs indicating road regions is justified against the vanishing point computed in the scene and a heading vector is computed that is to keep the robot on the road. As a result, it is shown that, even though a robot cannot fully rely on camera images for heading computation as proposed, use of image based heading computation can provide a useful assistance to other sensors present on a mobile robot.