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Communication Dans Un Congrès Année : 2023

Reinforcement Learning for Truck Eco-Driving: A Serious Game as Driving Assistance System

Résumé

Making fuel-economy for vehicles is an important and current challenge in particular for professionals of transportation. In this article, we address the challenge of providing a driving serious game based on artificial intelligence in order to significantly reduce the fuel consumption for trucks. Our proposition is based on a machine learning process consisting of a Self-Organizing Network (SOM) for clustering and subsequent reinforcement learning to deliver precise recommendations for eco-driving. Driving experts provide us knowledge in order to model the actions-rewards process. Experiments conducted on simulated data demonstrate that the recommendations are coherent and enable drivers to adopt eco-driving behavior.
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hal-04289959 , version 1 (04-04-2024)

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Mohamed Fassih, Anne-Sophie Capelle-Laizé, Philippe Carré, Pierre-Yves Boisbunon. Reinforcement Learning for Truck Eco-Driving: A Serious Game as Driving Assistance System. ACIVS 2023 (Advanced Concepts for Intelligent Vision Systems), Aug 2023, Kumamoto, Japan. pp.299-310, ⟨10.1007/978-3-031-45382-3_25⟩. ⟨hal-04289959⟩
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