19th AIAI 2023, 14 - 17 June 2023, León, Spain

Generating synthetic vehicle speed records using LSTM

Jiri Vrany, Michal Krepelka, Matej Chumlen


  Quality assurance testing of automotive electronic components such as navigation or infotainment displays requires data from genuine car rides. However, traditional static on-site testing methods are time-consuming and costly. To address this issue, we present a novel approach to generating synthetic ride data using Bidirectional LSTM, which offers a faster, more flexible, and environmentally friendly testing process. In this paper, we demonstrate the effectiveness of our approach by generating synthetic vehicle speed along a given route and evaluating the fidelity of the generated output using objective and subjective methods. Our results show that our approach achieves high levels of fidelity and offers a promising solution for quality assurance testing in the automotive industry. This work contributes to the growing research on generative machine learning models and their potential applications in the automotive industry.  

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