An Efficient Learning Assistant for a Contextual Road Navigation | Zendy

JeanMichel Ilié | Zendy; Karim Lahiani | Zendy; AhmedChawki Chaouche | Zendy; François Pêcheux | Zendy

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An Efficient Learning Assistant for a Contextual Road Navigation

Author(s) -

JeanMichel Ilié,

Karim Lahiani,

AhmedChawki Chaouche,

François Pêcheux

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.03.119

Subject(s) - computer science , abstraction , cache , context (archaeology) , computation , mechanism (biology) , shortest path problem , artificial intelligence , machine learning , theoretical computer science , algorithm , computer network , graph , paleontology , philosophy , epistemology , biology

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.

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