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Detection and avoidance of simulated potholes in autonomous vehicle navigation in an unstructured environment

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.