Data labeling is essential for training AI models in autonomous vehicles. It involves annotating images or sensor data with labels that describe objects, lanes, or traffic signs, enabling the models to learn and generalize effectively.
Techniques like bounding boxes or semantic segmentation are used to mark objects such as pedestrians, cars, and road features. This labeled data trains perception systems to detect and classify objects in real-world scenarios.
High-quality labeled data ensures accurate and safe autonomous driving, and platforms like Scale AI and Appen specialize in providing annotation services tailored to the automotive industry.