| Citation: | LUO Sheng, ZHAO Li, WANG Muchouet al. Lane semantic analysis based on road feature information[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1643-1649. doi: 10.13700/j.bh.1001-5965.2020.0079(in Chinese)
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Law enforcement on express roads in moving car requires to semantically analyze the road by lane detection algorithm, but the accuracy and recall rate of the algorithms based on human-crafted features are not good enough, and the algorithms based on deep learning require too much computing resource. Therefore, this paper proposes a semantical analysis algorithm based on road feature information. The proposed algorithm makes use of the gradient statistical information of edge points to filter out the candidate points in Hough space, and dynamic programming to find the most reasonable solution of lane line combination among the remaining candidate points. Thus it can accurately find all lane markings on roads with less computing resource. The experiment with self acquisition of data shows that the proposed method can structurally find all lanes on structured and unstructured roads. In a comparative experiment, contrasted with some other traditional lane detection methods and some deep learning networks, the proposed algorithm demonstrates its improvement in accuracy, recall rate and computing speed.
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