Maneuver Detection

Maneuver detection aims to automatically identify and classify driving actions from vehicle data, such as kinematic information or sensor readings, for applications like driver monitoring and autonomous vehicle training. Current research focuses on developing robust and efficient algorithms, including those employing space-filling curves for data compression, contrastive and generative self-supervised learning for unsupervised event discovery, and segmentation-classification pipelines using various machine learning models (e.g., convolutional neural networks, recurrent neural networks) to improve accuracy and transferability across datasets. This field is crucial for advancing safety and automation in transportation, enabling improved driver assistance systems and more reliable training data for autonomous driving technologies.