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      Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation

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          Abstract

          Lane detection is very important for self-driving vehicles. In recent years, computer stereo vision has been prevalently used to enhance the accuracy of the lane detection systems. This paper mainly presents a multiple lane detection algorithm developed based on optimised dense disparity map estimation, where the disparity information obtained at time t_{n} is utilised to optimise the process of disparity estimation at time t_{n+1}. This is achieved by estimating the road model at time t_{n} and then controlling the search range for the disparity estimation at time t_{n+1}. The lanes are then detected using our previously published algorithm, where the vanishing point information is used to model the lanes. The experimental results illustrate that the runtime of the disparity estimation is reduced by around 37% and the accuracy of the lane detection is about 99%.

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          Most cited references7

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          Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

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            Robust Road Modeling and Tracking Using Condensation

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              Faster-than-real-time linear lane detection implementation using SoC DSP TMS320C6678

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                Author and article information

                Journal
                28 August 2018
                Article
                1808.09128
                f49e2630-d5d2-4215-a732-4a3c21045b7a

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                5 pages, 7 figures, IEEE International Conference on Imaging Systems and Techniques (IST) 2018
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

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