Dataset for testing and training of map-matching algorithms

Abstract : We present a large-scale dataset for testing, benchmarking, and offline learning of map-matching algorithms. For the first time, a large enough dataset is available to prove or disprove map-matching hypotheses on a world-wide scale. There are several hundred map-matching algorithms published in literature, each tested only on a limited scale due to difficulties in collecting truly large scale data. Our contribution aims to provide a convenient gold standard to compare various map-matching algorithms between each other. Moreover, as many state-of-the-art map-matching algorithms are based on techniques that require offline learning, our dataset can be readily used as the training set. Because of the global coverage of our dataset, learning does not have to be be biased to the part of the world where the algorithm was tested.
Type de document :
Communication dans un congrès
2015 IEEE Intelligent Vehicles Symposium (IV'15), Jun 2015, Séoul, South Korea. 2015, Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV'15). 〈10.1109/ivs.2015.7225829 〉
Liste complète des métadonnées

https://hal-centralesupelec.archives-ouvertes.fr/hal-01284964
Contributeur : Myriam Baverel <>
Soumis le : mardi 8 mars 2016 - 12:18:27
Dernière modification le : jeudi 5 avril 2018 - 12:30:23

Identifiants

Citation

M. Kubicka, Arben Cela, Philippe Moulin, Hugues Mounier, Silviu-Iulian Niculescu. Dataset for testing and training of map-matching algorithms. 2015 IEEE Intelligent Vehicles Symposium (IV'15), Jun 2015, Séoul, South Korea. 2015, Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV'15). 〈10.1109/ivs.2015.7225829 〉. 〈hal-01284964〉

Partager

Métriques

Consultations de la notice

169