Vehicle Speed

Vehicle speed estimation is a crucial area of research with applications ranging from improving road safety and traffic management to enhancing medical imaging techniques. Current research focuses on developing robust and accurate speed estimation methods using diverse data sources, including video from cameras and smartphones, audio recordings, and ultrasound signals. Prominent approaches leverage deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and implicit neural representations (INRs), often combined with techniques like digital twin simulation to address data scarcity issues. These advancements offer the potential for more efficient traffic monitoring, improved diagnostic capabilities in medical imaging, and enhanced autonomous driving systems.