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Deep Learning based Vehicular Mobility Models for Intelligent Transportation Systems

Abstract : The intelligent transportation systems gain great research interests in recent years. Although the realistic traffic simulation plays an important role, it has not received enough attention. This thesis is devoted to studying the traffic simulation in microscopic level, and proposes corresponding vehicular mobility models. Using deep learning methods, these mobility models have been proven with a promising credibility to represent the vehicles in real-world. Firstly, a data-driven neural network based mobility model is proposed. This model comes from real-world trajectory data and allows mimicking local vehicle behaviors. By analyzing the performance of this basic learning based mobility model, we indicate that an improvement is possible and we propose its specification. An HMM is then introduced. The preparation of this integration is necessary, which includes an examination of traditional dynamics based mobility models and the adaptation method of “classical” models to our situation. At last, the enhanced model is presented, and a sophisticated scenario simulation is built with it to validate the theoretical results. The performance of our mobility model is promising and implementation issues have also been discussed
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Submitted on : Tuesday, May 21, 2019 - 9:57:34 PM
Last modification on : Tuesday, September 29, 2020 - 12:24:07 PM


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  • HAL Id : tel-02136219, version 1



Jian Zhang. Deep Learning based Vehicular Mobility Models for Intelligent Transportation Systems. Automatic Control Engineering. Ecole Centrale de Lille, 2018. English. ⟨NNT : 2018ECLI0015⟩. ⟨tel-02136219⟩



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