Speed Estimation

Speed estimation research focuses on accurately determining vehicle speeds using diverse data sources, aiming to improve traffic management, road safety, and autonomous navigation. Current efforts leverage deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like GRUs), and transformer-based architectures, often combined with techniques like object detection and depth estimation, to process data from cameras, sensors (e.g., IMUs, LiDAR, radar), and even audio recordings. These advancements address limitations of traditional methods and enable more robust and cost-effective speed estimation across various conditions, contributing to safer and more efficient transportation systems. The development of large, high-quality datasets, including synthetic data generated via digital twins, is also a key area of focus to improve model training and generalization.