Outdoor vision-based obstacle avoidance for autonomous land vehicle using fuzzy logic

In this paper, a hierarchical fuzzy navigating system only based on image for obstacle avoidance similar to the capability of human vision is proposed. The system applies Sugeno type fuzzy model to obstacle avoidance of the autonomous land vehicle (ALV) navigating. During the ALV navigating with the stereo vision camera, the fuzzy navigating system adopts the insufficient information (such as imprecise view angle and rough depth, etc.) to evaluate the best feasible steering direction. Even through one or more obstacles lie on a road surface and the road are separated into several side roads, the ALV still can avoid these obstacles and infer the best steering direction toward the wider side road. The experimental results clearly show that the ALV can navigate well in the outdoor scene and demonstrates the feasibility and the applicability of the proposed method by a series of images of the public video on YouTube.