A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data
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AuthorNavarro Lorente, Pedro Javier; Fernández Andrés, José Carlos; Borraz Morón, Raúl; Alonso Cáceres, Diego
Research GroupDivisión de Sistemas en Ingeniería Electrónica (DSIE)
Knowledge AreaLenguajes y Sistemas Informáticos
SponsorsThis work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de Educación, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016 for grant CAS14/00238.
PublisherEd. Molecular Diversity Preservation International (MDPI)
Bibliographic CitationNavarro, Pedro J et al. “A machine learning approach to pedestrian petection for autonomous vehicles using High-Definition 3D Range Data.” Sensors (Basel, Switzerland) vol. 17,1 18. 23 Dec. 2016, doi:10.3390/s17010018
3D LIDAR sensor
This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and ...
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