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.
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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 Online
Fully managed cloud platform. Sign up and start immediately. Free, Solo, and Team plans.
CVAT Enterprise
Self-hosted in your own infrastructure. SSO, RBAC, and enterprise support.
CVAT Community
Open-source, self-hosted edition. Free forever, full control of your stack.
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.
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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

At e2m, we use CVAT to review computer vision detections of animals in massive camera trap image datasets. We found CVAT to be the best tool at this scale, enabling efficient image review and robust quality control.

Joshua Wilson

Senior Computational Ecologist

tbmaestro uses CVAT as a key tool to structure large-scale annotations for equipment detection on our inspection photos and to accelerate computer vision deployment in asset management.

Anthony Renard

AI Engineer

Our mission is to make vessels safer for every crew at sea. CVAT plays a critical role in that, helping us transform raw CCTV footage from maritime environments into the training data for our AI detection systems. From identifying unsafe behaviour to surfacing near-miss situations before they escalate, CVAT is the foundation that makes our safety intelligence possible.

Vladimir Ponomarev

CEO

CVAT helps us label sports images to build high-quality ground truth that enables our AI to analyze volleyball performance and provide personalized feedback. It gives us the flexibility to combine automated and human labeling, and makes it easier to test different models and refine the best approach for our AI.

Fabian van Schevikhoven

Founder

At Arboair, we annotate forestry imagery to deliver world-class tree analysis worldwide, and CVAT has been key to making that possible at scale. Its ease of use and flexibility across annotation use cases support our full workflow, from production models to experimental projects.

Jacob Hjalmarsson

COO

Vimaan uses CVAT as its primary data annotation tool and selected this tool after a thorough evaluation of alternative tools both in open source and closed domain. Top 3PLs and warehouses have improved their inventory accuracy, reduced mis-shipments, improved bin utilization, while reducing overall resource.

Sudhir Kumar Singh

CTO & Chief AI Scientist

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.
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OTHER SOLUTIONS
Explore other CVAT solutions
From aerial imagery analysis and infrastructure inspection to insurance damage assessment, thousands of ML teams trust CVAT for labeling and managing their training data.

Geospatial & Remote Sensing

Geospatial analytics companies and urban planning teams use CVAT to annotate aerial imagery, such as outlining buildings, roads, and land use zones for mapping and planning applications.

Infrastructure & Utilities

Energy companies and infrastructure inspection teams use CVAT to annotate drone and camera footage, such as identifying defects on solar panels, power lines, or oil and gas pipelines.

Transportation & Logistics

Logistics companies and warehouse automation teams use CVAT to annotate imagery from robots and cameras, such as identifying packages, reading labels, or detecting obstacles on warehouse floors.

Autonomous Vehicles

AV companies, drone manufacturers, and robotics teams use CVAT to label camera and LiDAR data, such as marking cars, pedestrians, road signs, and lane boundaries to train self-driving and autonomous flight models.

Robotics

Robotics companies and research teams use CVAT to annotate imagery and sensor data, such as labeling objects a robot needs to grasp, navigate around, or interact with in real-world environments.

Military & Defense

Defense contractors and government agencies use CVAT to annotate satellite and drone imagery, such as identifying vehicles, structures, and terrain features in surveillance zones.

Manufacturing

Quality control and industrial automation teams use CVAT to annotate factory footage and imagery, such as marking surface defects on products or identifying misaligned parts on assembly lines.

Healthcare & Medical

Medical AI startups and hospital research teams use CVAT to segment anatomical structures, annotate tumors in radiology scans, label facial landmarks for diagnostic and clinical research models.

Retail & Ecommerce

Retail tech teams and ecommerce platforms use CVAT to annotate in-store and product imagery, such as labeling items on shelves or tagging visual attributes for catalog search.

Surveillance & Security

Security software companies and smart city teams use CVAT to annotate camera footage, such as marking people, vehicles, and suspicious activity across public and private spaces.

Smart Cities

City governments and urban technology companies use CVAT to annotate footage from public infrastructure, such as identifying vehicles and pedestrians at intersections or monitoring the usage of public spaces.

Agriculture

Agtech companies and research institutions use CVAT to label field and drone imagery, such as identifying diseased leaves, counting fruit, or mapping crop coverage across large areas.

Construction

Construction companies and site safety teams use CVAT to annotate drone and camera footage, such as detecting equipment, monitoring worker safety compliance, or tracking site progress over time.

Sports & Media

Sports analytics companies and broadcast technology teams use CVAT to annotate video footage, such as tracking players and balls across frames or identifying key moments in a match.

Environmental Monitoring

Environmental organizations and research teams use CVAT to annotate imagery across land, air, and water, such as tracking wildlife populations, detecting invasive species, or monitoring coral reef health.

Research & Academia

University labs and research institutions use CVAT to annotate data for a wide range of computer vision research, such as labeling microscopy images or building custom datasets for novel scientific questions.

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Beyond Vision: CVAT Goes Multimodal and Introduces Audio Annotation.Learn more ➔