Self-Driving Car

Abstract

Abstract: Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are of great interest in the context of Advanced Driver Assistance Systems. Among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. In driving assistance systems, obstacle detection especially for moving object detection is a key component of collision avoidance. Many sensors can be used for obstacle detection and lane detection, such as laser, radar and vision sensors. The most frequently used principal approach to detect road boundaries and lanes using a vision system on the vehicle. Detecting all kinds of obstacles on the road, mainly include the IPM (Inverse Perspective Mapping) method. The system acquires the front view using a camera mounted on the vehicle then applying a few processes in order to detect the lanes and objects. A versatile methodology is used in order to detect the lanes and objects. In our research we have developed a simple heuristic method which is more robust in both lane detection object detection and tracking in video. In this method we use clustering methodology to group the detected points in case of lane detection. Heuristic gives effective results in detection and tracking of multiple vehicles at a time irrespective to the distance.