data labelling
Project Scope The project focuses on generating high-quality labeled datasets to train, validate, and test machine learning models for autonomous vehicle perception systems. This includes annotating sensor data from multiple modalities such as cameras (RGB, infrared), LiDAR, radar, and ultrasonic sensors. The ultimate goal is to improve object detection, tracking, segmentation, and scene understanding for safe and reliable autonomous driving. Key objectives: Enable accurate detection of vehicles, pedestrians, cyclists, and static obstacles. Support advanced driver assistance system (ADAS) features like lane keeping, traffic sign recognition, and collision avoidance. Provide annotated datasets for deep learning models used in perception, planning, and navigation. Specific Data Labeling Tasks 2D Image Annotation (Camera Data) Bounding boxes for vehicles, pedestrians, cyclists, traffic signs, traffic lights, and road markings. Semantic segmentation for drivable areas, lanes, side