A Generative Adversarial Network Framework for Generating Synthetic Road Condition Data from Accelerometers

This research demonstrates a novel approach for efficiently generating realistic synthetic road condition data using Generative Adversarial Networks (GANs).Due to the challenges and dangers of manual data collection, we propose leveraging GANs to augment limited real-world road condition data from accelerometers.The real accelerometer data is pre-processed and used to train a GAN containing convolutional and dense layers.Qualitative analysis reveals the visual realism of the generated road condition data.Quantitative evaluation also demonstrates the high accuracy of the GAN, with precision and recall scores exceeding 0.9.Overall, this research highlights the promise of using GANs to synthesize safety-critical driving data, circumventing the need for exhaustive manual data collection.Our proposed framework can enable further research and applications in road condition monitoring and autonomous driving.