A Fast Lane and Vehicle Detection Approach for Autonomous Vehicles

B.-F. Wu, C.-T. Lin, C.-J. Chen, T.-C. Lai, H.-L. Liao, and A. Wu (Taiwan)


Lane, Obstacle detection, vision, autonomous vehicle


This paper proposes a lane detection algorithm, a fuzzy based vehicle detection approach and a distance measurement method for intelligent autonomous vehicles. The lane detection algorithm reduces computational load and has proven successful on highway and in urban settings at different velocities (110km/h and 50km/h, respectively). The proposed fuzzy-based vehicle detection approach named Contour Size Similarity (CSS), compares the contour size of objects by fuzzy rules. CSS efficiently distinguishes target obstacles, namely vehicles, from other objects detected and differentiates between obstacles and pattern on the road surface. After the nearest vehicle in the lane is found, we estimate its distance to the intelligent autonomous vehicle according to its position in the image. Then the information obtained would sever as aids for automatic driving of the intelligent autonomous vehicles.

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