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Sensors 2017, 17(10), 2359; https://doi.org/10.3390/s17102359

A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

1
Federal Institute of Education, Science and Technology of Espirito Santo, Serra ES 29173-087, Brazil
2
Department of Electrical Engineering, Federal University of Espirito Santo, Vitória ES 29075-910, Brazil
3
Institute of Engineering, Hanze University of Applied Sciences, Assen 9403AB, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 12 August 2017 / Revised: 27 September 2017 / Accepted: 28 September 2017 / Published: 16 October 2017
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)

Abstract

Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle’s backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation. View Full-Text
Keywords: autonomous driving; computer vision; lane marking detector; inertial navigation system; dead reckoning; data fusion; ego-localization; map matching autonomous driving; computer vision; lane marking detector; inertial navigation system; dead reckoning; data fusion; ego-localization; map matching
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Vivacqua, R.; Vassallo, R.; Martins, F. A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application. Sensors 2017, 17, 2359.

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