In this paper, we propose a conceptual framework where a centralized system, classifies the road based upon the level of damage. The centralized system also identifies the traffic intensity thereby prioritizing the roads that need quick action to be taken upon. Moreover, the system helps the driver to detect the level of damage to the road stretch and route the vehicle from an alternative path to its destination. The system sends a feedback to the concerned authorities for a quick response to the condition of the roads. The system we use comprises a laser sensor and pressure sensors in shock absorbers to detect and quantify the intensity of the pothole, a centralized server which maintains a database of locations of all the potholes which can be accessed by another unit inside the vehicle. A point to point connection device is also installed in vehicles so that, when a vehicle detects a pothole which is not in the database, all the vehicles within a range of 20 meters are warned about the pothole. The system computes a route with least number of potholes which is nearest to the desired destination . If the destination is unknown, then the system will check for potholes in the current road and displays the level of damage. The system is flexible enough that the destination can be added, removed or changed any time during the travel. The best possible route is suggested by the system upon the alteration. We prove that the algorithm returns an efficient path with least number of potholes.
An obstacle avoidance system was developed for a custom-made autonomous navigational robotic vehicle (ANROV), based on an intelligent sensor network and fuzzy logic control. Unlike conventional crisp control systems, this system is based on soft computing, such that the system mimics human decision making processes and can better recognize obstacles and decide the best course of action. The fuzzy logic control system was developed in MATLAB, the GUI was designed in LabView, and the hardware realization was installed in ANROV for evaluation and development In the navigation of an autonomous vehicle, tracking and avoidance of the obstacles presents an interesting problem as this involves the integration of the vision and the motion systems. In an unstructured environment, the problem becomes much more severe as the obstacles have to be clearly recognized for any decisive action to be taken. In this paper, we discuss a solution to detection and avoidance of simulated potholes in the path of an autonomous vehicle operating in an unstructured environment. Pothole avoidance may be considered similar to other obstacle avoidance except that the potholes are depressions rather than extrusions form a surface. A non-contact vision approach has been taken since potholes usually are significantly different visually from a background surface. Large potholes more than 2 feet in diameter will be detected. Furthermore, only white potholes will be detected on a background of grass, asphalt, sand or green painted bridges. The signals from the environment are captured by the vehicle's vision systems and pre-processed appropriately. A histogram is used to determine a brightness threshold to determine if a pothole is within the field of view. Then, a binary image is formed. Regions are then detected in the binary image. Regions that have a diameter close to 2 feet and a ratio of circumference to diameter close to pi are considered potholes. The neuro-fuzzy logic controller where navigational strategies are evaluated uses these signals to decide a final course of navigation. The primary significance of the solution is that it is interfaced seamlessly into the existing central logic controller. The solution can also be easily extended to detect and avoid any two dimensional shape.