Abstract

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element. The research leading to these results has received funding from the “HERMES-Smart Driver” Project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “Analytics Using Sensor Data for Flatcity” Project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by the SpanishAgencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).

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The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2017/3802807.xml,
http://dx.doi.org/10.1155/2017/3802807 under the license http://creativecommons.org/licenses/by/4.0
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
https://doi.org/10.1155/2017/3802807
http://downloads.hindawi.com/journals/jat/2017/3802807.pdf,
https://doi.org/10.1155/2017/3802807,
https://core.ac.uk/display/88193586,
https://academic.microsoft.com/#/detail/2737571926 under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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Published on 01/01/2017

Volume 2017, 2017
DOI: 10.1155/2017/3802807
Licence: Other

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