Data augmentation to enhance the accuracy of deep neural networks in subjective vehicle dynamics evaluation

Key Points

• Deep neural networks achieve higher accuracy in evaluating vehicle dynamics with data augmentation techniques.
• The evaluation of subjective vehicle dynamics shows improved accuracy using augmented datasets, achieving notable advancements.
• Analysis of data augmentation effects on deep neural networks highlights their role in enhancing model performance.
• Improved deep learning models may lead to better insights in vehicle dynamics evaluation, highlighting the need for diverse data.

Abstract