Course Content

The autonomous vehicle will have a vision-based system. Computer vision algorithm that detects and tracks the boundaries of drivable regions appearing on input images. This makes the vehicle make use of dynamic input, frame-by-frame. Hence, by combining it with other methods and functions like Hough transform and canny edge detection help us reduce the error rate and give better outputs. Even though computer vision is used in industry, recent development of precision requirements, due to regulations, computer vision has more importance and has become obligation in the industry.

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle’s heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements.Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.

In intelligent transportation systems, intelligent vehicle cooperate with smart infrastructure to achieve a safer environment and batter traffic conditions. Although, a more convincing reason to build intelligent vehicles is to improve the safety conditions by the entire or partial automation of driving tasks. Among these tasks, the road detection took an important role in driving assistance systems that provides information such as lane structure and vehicle position relative to the lane. However, the most compelling reason for adding autonomous capability to vehicles that to ensure the safety requirement. Vehicle crashes remain the leading cause of accident death and injuries in Malaysia and Asian countries claiming tens of thousands of lives and injuring millions of people each year. Most of these transportation deaths and injuries occur on the nation’s highways. The United Nations has ranked Malaysia 30th among countries with the highest number of fatal road accidents, registering an average of 4.5 deaths per 10,000 registered vehicles [1]. Therefore, a system that provides a means of warning the driver to the danger has the potential to save a considerable number of lives. One of the main technology involves in these takes computer vision which become a powerful tool for sensing the environment and has been widely used in many application by the intelligent transportation systems (ITS) In many proposed systems[2], the lane detection consists of the localization of specific primitives such as the road markings of the surface of painted roads. This restriction simplifies the process of detection, nevertheless, two situations can disturb the process: the presence of other vehicles on the same lane occluded partially the road markings ahead of the vehicle are the presence of shadows caused by trees, buildings etc. This paper presents vision- based approach capable of reaching a real time performance in detection and tracking of structured road boundaries (painted or unpainted lane markings) with slight curvature, which is robust enough in presence of shadow conditions. Road boundaries are detected by fitting a parallel hyperbola pairs to the edges of the lane after applying the edge detection and Hough transform. The vehicle is supposed to move on a flat and straight road or with slow curvature.