J.-M. Ilié, K. Lahiani, A.-C. Chaouche and F. Pêcheux. 2020. An Efficient Learning Assistant for a Contextual Road Navigation. In Proceedings of the 11th International Conference on Ambient Systems, Networks and Technologies (ANT'2020), Warsaw, Poland. Procedia Computer Science, 170 (April 2020), Elsevier, 522-529. DOI: https://doi.org/10.1016/j.procs.2020.03.119.

Ilié, J.-M., Lahiani, K., Chaouche, A.-C. & Pêcheux, F. (2020, April). An Efficient Learning Assistant for a Contextual Road Navigation. In Proceedings of the 11th International Conference on Ambient Systems, Networks and Technologies (ANT'2020), Warsaw, Poland. Procedia Computer Science, 170, Elsevier, 522-529. DOI: https://doi.org/10.1016/j.procs.2020.03.119

J.-M. Ilié, K. Lahiani, A.-C. Chaouche and F. Pêcheux, "An Efficient Learning Assistant for a Contextual Road Navigation". In Proceedings of the 11th International Conference on Ambient Systems, Networks and Technologies (ANT'2020), Warsaw, Poland. Procedia Computer Science, vol. 170, Elsevier, pp. 522-529, April, 2020. DOI: https://doi.org/10.1016/j.procs.2020.03.119.

@inproceedings{misc-lab-259,
author = {Ili\'{e}, Jean-Michel and Lahiani, Karim and Chaouche, Ahmed-Chawki and P\^{e}cheux, François},
title = {An Efficient Learning Assistant for a Contextual Road Navigation},
volume = {170},
booktitle = {11th International Conference on Ambient Systems, Networks and Technologies (ANT'2020)},
series = {Procedia Computer Science},
location = {Warsaw, Poland},
pages = {522--529},
publisher = {Elsevier},
year = {2020},
month = {April},
doi = {10.1016/j.procs.2020.03.119},
url = {http://www.sciencedirect.com/science/article/pii/S1877050920305573},
keywords = {Context awareness, Intelligent transportation systems, Learning systems, Cache memory, Path planning, Clustering}
}

TY  - CONF
TI  - An Efficient Learning Assistant for a Contextual Road Navigation
AU  - J.-M. Ilié
AU  - K. Lahiani
AU  - A.-C. Chaouche
AU  - F. Pêcheux
PY  - 2020
VL  - 170
BT  - 11th International Conference on Ambient Systems, Networks and Technologies (ANT'2020), Warsaw, Poland
SP  - 522
EP  - 529
PB  - Elsevier
AB  - Due to traffic conditions that are very context dependent, the computation of optimized or shortest paths is a very complex problem for both drivers and autonomous vehicles. In this paper, we introduce a learning mechanism that is able to efficiently evaluate path durations based on an abstraction of the available traffic information. We demonstrate that a cache data structure allows a permanent access to the results whereas a lazy politics taking new data into account is used to increase the viability of those results. Our measures highlight the performance of each mechanism, according to different learning strategies.
KW  - Context awareness
KW  - Intelligent transportation systems
KW  - Learning systems
KW  - Cache memory
KW  - Path planning
KW  - Clustering
DO  - 10.1016/j.procs.2020.03.119
UR  - http://www.sciencedirect.com/science/article/pii/S1877050920305573
ID  - misc-lab-259
ER  -