Traffic Bottleneck Reconstruction LIDAR Orthoimages: A RANSAC Algorithm Feature Extraction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Nazmus Sakib , Ashiqur Rahman

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. More... »

PAGES

302-307

References to SciGraph publications

  • 2018. Regression Analysis in MARKET RESEARCH
  • Book

    TITLE

    Recent Trends in Data Science and Soft Computing

    ISBN

    978-3-319-99006-4
    978-3-319-99007-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-99007-1_29

    DOI

    http://dx.doi.org/10.1007/978-3-319-99007-1_29

    DIMENSIONS

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