Abstract
This study attempts a solution for autonomous vehicles to avoid immediate collision due to close proximity between cars. Since LIDAR sensors are widely used for capturing images in autonomous car industry, we depict a scope of using RANSAC algorithm and linear regression to reconstruct the orthoimages to escape traffic bottleneck as well as avoid collision. It is found that LIDAR sensors can’t suggests much detail in close distance, and cameras don’t perform well in conditions with low light or glare images. Dataset is collected from KITTI (Karlsruhe Institute of Technology) containing compressed pixels. Significance resultants focus on error reduction followed by feature extraction simulated with MATLAB. The findings excludes large scale of data size to implement and project in T-way testing for determining strength as well as capability of resultants.
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Sakib, M.N., Rahman, M.A. (2019). Traffic Bottleneck Reconstruction LIDAR Orthoimages: A RANSAC Algorithm Feature Extraction. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_29
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DOI: https://doi.org/10.1007/978-3-319-99007-1_29
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