Autonomous Vehicle Annotation Project (Remotask)
The Autonomous Vehicle Annotation Project involved creating high-quality labeled datasets from image, video, and LiDAR data to support training and evaluation of self-driving systems. It covered diverse real-world driving scenarios and focused on perception tasks such as object detection, segmentation, tracking, and scene understanding. Key annotation activities included 2D and 3D bounding boxes, semantic and instance segmentation, lane and keypoint annotation, object tracking, sensor fusion, and attribute labeling (e.g., object states, traffic signals, and environmental conditions). Quality was ensured through detailed guidelines, trained annotators, multi-level quality reviews, automated validation checks, and strict accuracy and consistency standards, resulting in reliable and scalable data for safe and effective autonomous driving models.