Abstract

utonomous route following with road vehicles has gained popularity in the last few decades. In order to provide highly automated driver assistance systems, different types and combinations of sensors have been presented in the literature. However, most of these approaches apply quite sophisticated and expensive sensors, and hence, the development of a cost-efficient solution still remains a challenging problem. This work proposes the use of a single monocular camera sensor for an automatic steering control, speed assistance for the driver and localization of the vehicle on a road. Herein, we assume that the vehicle is mainly traveling along a predefined path, such as in public transport. A computer vision approach is presented to detect a line painted on the road, which defines the path to follow. Visual markers with a special design painted on the road provide information to localize the vehicle and to assist in its speed control. Furthermore, a vision-based control system, which keeps the vehicle on the predefined path under inner-city speed constraints, is also presented. Real driving tests with a commercial car on a closed circuit finally prove the applicability of the derived approach. In these tests, the car reached a maximum speed of 48 km/h and successfully traveled a distance of 7 km without the intervention of a human driver and any interruption.

The work reported in this paper is the product of several research stages at the Computer Vision Group Universidad Politécnica de Madrid in collaboration with INSIA-UPM and Siemens España S.A. This project is partially funded by the Centre for the Development of Industrial Technology (CDTI). The authors would like to thank the company SIEMENS España S.A. that has made possible the research described in this paper through several contracts and the people at LABIE (INSIA-UPM) for their support.

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http://www.mdpi.com/1424-8220/16/3/362 under the license cc-by
https://doaj.org/toc/1424-8220
http://dx.doi.org/10.3390/s16030362
https://www.mdpi.com/1424-8220/16/3/362/pdf,
https://www.mdpi.com/1424-8220/16/3/362,
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https://orbilu.uni.lu/bitstream/10993/25600/1/Olivares_AutonomousDriving_sensors_16.pdf,
https://doaj.org/article/bdeea43c134341e0aa403ed1f2101046,
https://digital.csic.es/handle/10261/170732,
https://orbilu.uni.lu/handle/10993/25600,
https://doi.org/10.3390/s16030362,
https://trid.trb.org/view/1405277,
https://publications.uni.lu/handle/10993/25600,
[=citjournalarticle_515252_34 https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_515252_34],
https://core.ac.uk/display/89557845,
https://academic.microsoft.com/#/detail/2293484007 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2016

Volume 2016, 2016
DOI: 10.3390/s16030362
Licence: Other

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