Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are one 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[1]. 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 vision system on the vehicle. The detecting all kinds of obstacle on the road, mainly include IPM (Inverse Perspective Mapping) method. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes and objects. A versatile methodology is used in order to detecting 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.

Robust lane detection and object tracking is an important application of Intelligent Transport System. To avoid victims and number of accidents in heavy traffic countries like USA, China, Malaysia, UK, where it becomes difficult for the driver to exact location and detection of line and cars especially during cloudy environment than it is important to make Intelligent Transport System more robust and as well in other way lane detection and object tracking is one of important future application of auto drive vehicle. For instance, in our research we have developed a Heuristic Algorithm which is more robust in case of lane detection when compared with other methods of lane detection with reduced complexity, more tolerant to scene condition and also easy to implement in any noisy environment. In the same manner it is also in object tracking. Multiple vehicles are detected on the same time without any distortions and overcome all the drawbacks when compared with other methods. This method is very effective in all the conditions and more robust in object tracking with reduced complexity and easy to implement under different scene conditions, that significantly gives more strengthen to Intelligent Transport System.