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Open AccessArticle

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Felipe Jimenez
Sensors 2017, 17(1), 18; https://doi.org/10.3390/s17010018
Received: 31 October 2016 / Revised: 11 December 2016 / Accepted: 15 December 2016 / Published: 23 December 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
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 specificity (96.8%). View Full-Text
Keywords: pedestrian detection; 3D LIDAR sensor; machine vision and machine learning pedestrian detection; 3D LIDAR sensor; machine vision and machine learning
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MDPI and ACS Style

Navarro, P.J.; Fernández, C.; Borraz, R.; Alonso, D. A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data. Sensors 2017, 17, 18.

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