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

Generating synthetic vehicle speed records using LSTM

Jiri Vrany, Michal Krepelka, Matej Chumlen

Abstract:

  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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