Self-driving vehicles depend on their ability to perceive their environment for safe navigation. Experts play a key role by annotating bounding boxes, helping the vehicle's computer vision system accurately assess the distance and size of objects on the road and along the sides.
Semantic segmentation enhances object detection in autonomous vehicles by grouping multiple objects of the same category as a single unit. This process involves classifying every pixel in an image based on its semantic meaning, such as identifying all cars or all people within the scene.
Autonomous vehicles rely on environmental perception to navigate safely. Specialists contribute by marking bounding boxes, enabling the vehicle's computer vision system to accurately determine the size and distance of objects on and around the road.
Access expert assistance to accurately trace polygons around irregularly shaped objects. This enables computer vision systems in autonomous vehicles to detect all visible objects on the road, such as motorcycles, bicycles, cars, and animals, ensuring safe driving by preventing collisions.
Unitlab AI supports synchronized road video, camera images, geospatial imagery, text, documents, and related sensor records in one governed training-data workflow.
Teams can combine bounding boxes, polygons, segmentation, polylines, keypoints, classifications, properties, temporal ranges, and object tracks for perception and mobility models.
Yes. Frame-accurate video annotation, keyframes, interpolation, object tracking, temporal labels, and review workflows support long-form road and fleet footage.
Related camera views can be grouped and reviewed with shared temporal context, helping teams keep objects, events, and scene properties consistent across viewpoints.
Yes. Teams can search, filter, balance, deduplicate, version, and route selected road and mobility data into annotation and QA workflows.
Yes. Bring Your Own Model workflows let teams connect domain models with Unitlab automation, then review and correct predictions inside the annotation loop.
Instructions, ontologies, reviewer roles, issues, rework, approvals, and annotation history help teams enforce consistent quality across transportation datasets.
Unitlab supports large datasets, dataset versions, programmatic upload and export, and collaborative workflows for production-scale transportation programs.
On-premises deployment is available for teams that need transportation data, models, and workflows to remain inside controlled infrastructure.
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