An Efficient Learning Assistant for a Contextual Road Navigation

Jean-Michel Ilie (1) , Karim Lahiani (1) , Ahmed-Chawki Chaouche (2) , François Pêcheux (3)

1 MoVe - Modélisation et Vérification
2 MISC - Modélisation et d'Implémentation des Systèmes Complexes [Constantine]
3 CIAN - Circuits Intégrés Numériques et Analogiques

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.

Mots clés: Context awareness; Intelligent transportation systems; Learning systems; Cache memory; Path planning; Clustering

Domaines: Informatique ubiquitaire; Théorie et langage formel [cs.FL]; Performance et fiabilité [cs.PF]; Système multi-agents [cs.MA]

Jean-Michel Ilie, Karim Lahiani, Ahmed-Chawki Chaouche, François Pêcheux. An Efficient Learning Assistant for a Contextual Road Navigation. Procedia Computer Science, 2020, 170, pp.522-529. ⟨10.1016/j.procs.2020.03.119⟩. ⟨hal-03042134⟩