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

Pedro Navarro,Raúl Borraz,Diego Alonso,Carlos Fernández

doi:10.3390/s17010018

Pedro Navarro, Raúl Borraz + Show 2 more

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https://doi.org/10.3390/s17010018

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Abstract

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%).

Highlights

Autonomous driving is presented as a highly disruptive feature for road means of transport, capable of influencing aspects as fundamental as road safety and mobility itself
Since the proposed algorithm for pedestrian detection comprises a ML Algorithms (MLA), and we have tested the performance of k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM) algorithms, we divide this section into two parts: performance and selection of the MLA that will detect pedestrians in the cloud of points generated by the 3D Laser Imaging Detection and Ranging (LIDAR), and performance of the overall algorithm in a real scenario
2017, 17, 18of the MLA that will detect pedestrians in the cloud of points generated by the of3D

Summary

Introduction

Autonomous driving is presented as a highly disruptive feature for road means of transport, capable of influencing aspects as fundamental as road safety and mobility itself. An autonomous car is a vehicle that is capable of sensing its environment and navigating without human input [1]. Traffic safety will be improved, since autonomous systems, in contrast to human drivers, have faster reaction times and are fatigue-proof in their functioning. The number of traffic collisions, dead and injured passengers, and damage to the environment will be reduced. New business models, such as mobility as a service, which aims to be cheaper than owning a car, would be available

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