CVAT Data Annotation Solutions for Automotive & Mobility

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  Autonomous driving perception

  Label vehicles, pedestrians, cyclists, and other road agents across camera and LiDAR frames for detection, tracking, and motion prediction.

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  3D object detection and sensor fusion

  Annotate cuboids directly in point clouds and align them with synchronized camera views to train multi-sensor perception models.

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  Lane, road, and drivable surface segmentation

  Trace roads, railways, power lines, pipelines, and other assets for mapping, inspection, and maintenance workflows.

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  Traffic sign and signal recognition

  Detect and classify signs, signals, and road markings, capturing occlusion, damage, and validity as structured attributes.

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  Driver and in-cabin monitoring

  Label gaze direction, head pose, body keypoints, and occupant state for driver attention, drowsiness, and occupancy systems.

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  Edge case mining and ADAS validation

  Annotate rare scenarios, adverse weather, and near-miss events to close the gaps that dominate perception failure rates.

See how CVAT powers research in automotive

• Road scene understandingDAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable EnvironmentsResearchers from the University of Maryland, College Park, the University of Virginia, and Boston University used CVAT to annotate road users, actions, and maneuvers in complex driving scenes.Read on arXiv
• Pedestrian and cyclist trackingWiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle InteractionsResearchers from Texas A&M University and Hyundai Motor Company used CVAT to annotate and interpolate pedestrian and cyclist tracks around an autonomous shuttle.Read on arXiv
• Driver monitoringDeep Insight: A Cloud Based Big Data Analytics Platform For Naturalistic Driving Studies — Data Management, Analytics, and Automated AnnotationsResearchers from Iowa State University, the University of Nebraska Medical Center, and Syracuse University integrated CVAT into a driving research workflow to review and verify driver head-pose annotations.Read on J-STAGE
• Lane detectionA Hybrid Lane Detection Model for Wild Road ConditionsResearchers from the Indian Institute of Science and the Indian Institute of Information Technology, Kalyani, used CVAT to annotate lane markings for a dataset of challenging Indian road scenes.Read on IEEE Xplore

• “CVAT helped me build an AI-powered inventory counting solution for warehouse operations. The annotation workflow was efficient, and it made it much easier to create high-quality training data for accurate object detection and counting.”

  

  Sukanya Suman Kalyan

  Azure Data Engineer, Deloitte

• “We selected CVAT after evaluating several options and determined it was the best fit for our instance segmentation workflows. Features such as automatic bordering and bitmap visualization help us improve annotation quality while maintaining high productivity.”

  

  Jorge Rojas

  Ph.D. Candidate & Research Assistant, Purdue University

• “SIYTE is an AI-powered intelligence platform connecting cameras, sensors and systems with CVAT playing an instrumental role in supporting our data annotation and model training function. CVAT is a reliable and easy to use tool that genuinely supports how our teams build and refine computer vision models.”

  

  Jamie Gale

  Chief Technology Officer, SIYTE

• “At Dragonfly AI we help brands measure creative effectiveness of marketing materials e.g. packaging, advertisements etc. using patented AI. CVAT helps us efficiently track attention-critical areas in video ads and feed structured annotation data into our proprietary attention measurement algorithm. The platform is straightforward, reliable, and fits naturally into our workflow.”

  

  Will Champion

  Customer Success Executive, Dragonfly AI

• “We began our AI journey with CVAT because, at the time, it was the only solution truly bridging the gap between raw data capture and practical model training workflows. Years later, it continues to be a foundational part of our pipeline thanks to its robustness, flexibility, and deep alignment with real-world computer vision development needs.”

  

  Maikel Boerebach

  Business Development Manager, AKL

• “We chose CVAT for its strong balance of capability and cost control. It allows us to build and manage our annotation workflows without unnecessary overhead.”

  

  Craig Metcalfe

  COO/CPO, Illuminex AI