Time-series modelling (TSM) is a critical task for intelligent vehicles (IVs), covering areas like fault detection, health monitoring, and inference of road user intentions. In this study, we present a novel TSM approach for enhancing the accuracy of multi-variate signal forecasting in intelligent vehicles. Our method leverages advanced Transformer networks within a relativistic generative adversarial network (RGAN) training framework. The RGAN training framework efficiently improves the accuracy of vehicle states forecasting for IV, demonstrating effective learning of long-time dependencies for more accurate predictions over extended sequences. Additionally, we introduce a high-dimensional extension (HDE) built-in block for the time-series Transformer to explore the impact of higher-dimensional features on representing long-term sequences. The experimental data is collected from a real-world electric vehicle testing bed. We evaluate the proposed RGANFormer framework and the HDE block on two popular time-series models, namely, Autoformer and FiLM. The results demonstrate that the RGANFormer, along with the built-in HDE block, significantly enhances long-term sequential forecasting accuracy for both multivariate and univariate tasks.


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    Title :

    RGANFormer: Relativistic Generative Adversarial Transformer for Time-Series Signal Forecasting on Intelligent Vehicles


    Contributors:


    Publication date :

    2024-06-02


    Size :

    1181953 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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