AUTOMOTIVE & MOBILITY AI

CVAT Data Annotation Solutions for Automotive & Mobility

Turn multi-camera video, LiDAR point clouds, and radar capture into model-ready datasets for autonomous driving, ADAS, driver monitoring, and in-cabin perception.
Need to keep your data in your own infrastructure? Explore CVAT Enterprise
APPLICATIONS
Build high-quality training data for automotive AI tasks
Label road agents in 2D and 3D, segment lanes and drivable surface, identify traffic signs and signals, and monitor driver and occupant state across camera, LiDAR, and radar capture.
Autonomous driving perception
Label vehicles, pedestrians, cyclists, and other road agents across camera and LiDAR frames for detection, tracking, and motion prediction.
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.
Lane, road, and drivable surface segmentation
Trace roads, railways, power lines, pipelines, and other assets for mapping, inspection, and maintenance workflows.
Traffic sign and signal recognition
Detect and classify signs, signals, and road markings, capturing occlusion, damage, and validity as structured attributes.
Driver and in-cabin monitoring
Label gaze direction, head pose, body keypoints, and occupant state for driver attention, drowsiness, and occupancy systems.
Edge case mining and ADAS validation
Annotate rare scenarios, adverse weather, and near-miss events to close the gaps that dominate perception failure rates.
CVAT PLATFORM

Manage the full automotive annotation workflow in CVAT

Import multi-camera video, LiDAR point clouds, and radar capture, annotate in 2D and 3D, coordinate multi-stage review, and export model-ready datasets from one workspace.

1. Import data from local files and cloud storage

Upload local datasets and existing annotations
Connect Amazon S3, Azure Blob Storage, Google Cloud Storage, and other storages
Use S3-compatible buckets or self-hosted storage

2. Manage projects, tasks, jobs, and team access

Organize annotation work into projects, tasks, and jobs
Assign annotators, reviewers, and project contributors
Manage roles, permissions, stages, statuses, and task ownership

3. Label data with manual and automated tools

Create boxes, polygons, masks, skeletons, tags, and tracks
Speed up labeling with SAM 2, SAM 3, Ultralytics, Hugging Face models, or custom models
Support detection, segmentation, tracking, pose estimation, and other CV workflows

4. Validate annotations and control quality

Review jobs through validation and acceptance stages
Measure quality with ground truth, honeypots, and consensus workflows
Resolve issues and keep quality decisions traceable

5. Monitor task progress and team workload

Track progress across projects, tasks, and jobs
Monitor team workload and completion status
Identify bottlenecks and measure annotation and validation throughput

6. Export datasets and integrate CVAT workflows

Export data in 20+ formats, including COCO, YOLO, KITTI, Cityscapes, and Pascal VOC
Automate import, task management, and export through API, SDK, and CLI
Build custom integrations around CVAT data labeling and ML workflows
CVAT LABELING SERVICES
Have CVAT deliver your automotive training dataset
Provide your drive data, object taxonomy, annotation guidelines, and quality requirements. Our project managers, annotators, and validators handle setup, labeling, review, and final delivery.
300+ dedicated annotators across 12 time zones
Real-time progress dashboards and delivery tracking
Scales from 1,000 to 100M+ labels without you hiring a single person
Multi-stage QA with rework guarantees and accuracy benchmarks
WHY CVAT
Why choose CVAT for automotive data annotation?
Keep labels consistent across sensors, control where drive data is processed, and scale when perception workloads spike.
Consistent object identity across sensors
Align 3D cuboids in LiDAR point clouds with synchronized camera views, then maintain object identity across drive sequences with track mode and keyframe interpolation.
Keep drive data where your compliance team needs it
Use CVAT Online, deploy Enterprise in your infrastructure, or build on Community to meet data-residency, OEM confidentiality, and pre-release vehicle requirements.
Burst capacity for edge-case backlogs
Add CVAT Labeling Services for re-collections, new edge-case classes, or sudden backlogs without moving the project to a separate annotation system.
Research Use Cases
See how CVAT powers research in automotive
Road scene understanding
DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments
Researchers 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 tracking
WiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle Interactions
Researchers 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 monitoring
Deep Insight: A Cloud Based Big Data Analytics Platform For Naturalistic Driving Studies — Data Management, Analytics, and Automated Annotations
Researchers 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 detection
A Hybrid Lane Detection Model for Wild Road Conditions
Researchers 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
CUSTOMER REVIEWS
Why AI teams choose CVAT for labeling their data
4.5/5

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

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

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

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

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

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

Frequently Asked Questions

What types of automotive data can I annotate in CVAT?
CVAT can be used with multi-camera video, image sequences, and LiDAR point cloud data in supported formats. Teams commonly use it to build training data for autonomous driving perception, ADAS, traffic scene understanding, and in-cabin monitoring.
Can objects be tracked across a long drive sequence?
Yes. Track mode propagates an object across frames and interpolates between keyframes, maintaining a consistent identity for the full sequence. This matters for tracking and motion prediction datasets, where identity switches are a common source of label error.
Which annotation types are used for road scenes?
Road agents are usually boxes in 2D and cuboids in 3D. Lane markings and road boundaries are polylines; drivable surface and free space are polygons or masks. In-cabin work leans on skeletons and points for head pose and body keypoints. Occlusion, truncation, and object state are recorded as attributes rather than as separate classes, which keeps the class list small enough to train on.
Can we pre-label drive data with our existing perception model?
Yes. Import predictions from your current stack or connect a compatible custom model, then have annotators confirm, correct, or reject. Labeling the difference between your model and ground truth is usually faster than labeling from scratch, and the disagreements are the part of the dataset with real training signal.
Can CVAT read drive data directly from our cloud storage?
CVAT supports connected cloud storage including Amazon S3, Azure Blob Storage, Google Cloud Storage, and Backblaze B2, so sequences offloaded from a collection fleet can be annotated where they land instead of being staged a second time.
How do you stop identity switches in tracking datasets?
Track mode makes identity a property of the track rather than something re-asserted frame by frame, which removes most switches at the source. Review stages and ground-truth jobs then catch the ones that remain — they cluster around occlusion and re-entry, so those segments are worth reviewing first.
Can annotation be triggered automatically from our edge-case mining pipeline?
Yes. The REST API, Python SDK, and CLI let you create tasks, assign work, monitor status, and pull completed annotations programmatically, so mined scenarios can queue for labeling without anyone opening the interface.
Can CVAT Labeling Services absorb a spike in labeling volume?
Yes. The service covers setup, workforce allocation, annotation, review, quality control, and delivery against your taxonomy and acceptance criteria — which suits perception programmes where a re-collection or a new edge-case class adds months of work at short notice.
Can drive data stay inside our own infrastructure?
Yes. CVAT Labeling Services can handle project setup, workforce allocation, annotation, review, quality control, and final delivery against your taxonomy, guidelines, and acceptance criteria. This works for one-time mapping backlogs, seasonal surveys, and recurring regional or global monitoring programs.

Build your next

automotive dataset

with CVAT

Manage annotation with your own team or have CVAT deliver a validated dataset for your model.
Free plan available • No credit card required • GDPR & CCPA compliant
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Beyond Vision: CVAT Goes Multimodal and Introduces Audio Annotation.Learn more ➔