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.
Highlights
Autonomous driving is the highest level of automation for a vehicle, which means the vehicle can drive itself from a starting point to a destination with no human intervention
The first part of the tests was made in Brazil, with the objective of analyzing the error in the information required to perform autonomous driving (Section 6.2).The second part of the tests was made in Italy, with the objective of verifying, in real traffic conditions, the performance of autonomous driving based on our localization method
This paper presents a low cost sensors approach for accurate vehicle localization and autonomous driving application
Summary
Autonomous driving is the highest level of automation for a vehicle, which means the vehicle can drive itself from a starting point to a destination with no human intervention. To achieve the stringent level of accuracy, integrity, and availability, required for autonomous driving applications, outstanding projects [5,6,7,8,9,10,11,12,13] have been using widely two kinds of exteroceptive sensors: 3D LASER scanners (LIDAR) [14,15] and cameras Some of these projects [6,7] use prior information, for example a map, to compare the current sensor readings, which produces a more accurate, robust and reliable localization. Mapping is executed using the same set of sensors used in autonomous driving Another important contribution is the detailed presentation of how low-cost, standard commercial devices, like USB camera, ordinary GNSS receiver, MEMs based gyroscope and a regular notebook computer can be used to achieve accurate vehicle localization and autonomous driving.
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