Xing, YangKong, XiangqiTsourdos, Antonios2024-07-232024-07-232024-07-15Xing Y, Kong X, Tsourdos A. (2024) RGANFormer: Relativistic Generative Adversarial Transformer for time-series signal forecasting on intelligent vehicles. In: 2024 IEEE Intelligent Vehicles Symposium (IV), 2-5 June 2024, Jeju Island, Republic of Korea, pp. 3241-3247https://doi.org/10.1109/IV55156.2024.10588746https://dspace.lib.cranfield.ac.uk/handle/1826/22664Time-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.3241-3247enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Time-seriesgenerative adversarial networkTransformermulti-task learningConference paper