Autonomous Vehicle & Transportation AI Annotation
Build safer self-driving systems with precision perception data annotation from Australia's trusted autonomous vehicle labeling experts.
Why Autonomous Vehicle Annotation Quality Matters
Autonomous vehicles must perceive their environment with near-perfect accuracy—lives depend on it. Missed pedestrians, incorrectly labeled traffic signs, and inconsistent object tracking create perception failures that lead to accidents. AI Taggers delivers enterprise-grade annotation with automotive domain expertise that ensures your AV systems understand complex driving scenarios across all conditions.
Trusted by autonomous vehicle companies, ADAS developers, automotive OEMs, and robotaxi operators to annotate millions of driving scenes with safety-critical precision.
2D Image Annotation for Autonomous Driving
Pixel-perfect annotation for camera-based perception systems
Object Detection & Bounding Boxes
Annotate vehicles, pedestrians, cyclists, motorcyclists, and vulnerable road users with pixel-perfect bounding boxes across all vehicle types.
Semantic Segmentation
Pixel-level classification of road surfaces, lane markings, sidewalks, crosswalks, vegetation, buildings, sky, and drivable areas.
Instance Segmentation
Distinguish individual vehicles, pedestrians, and objects within the same class while maintaining unique instance identities.
Lane & Road Marking Annotation
Label lane boundaries, lane types, road edges, crosswalks, stop lines, yield lines, arrows, and road surface markings.
Traffic Sign & Signal Detection
Identify and classify traffic signs, traffic lights, and regulatory signage across diverse geographic regions and sign standards.
Occlusion & Truncation Handling
Expert annotation of partially visible objects, occluded pedestrians, and truncated vehicles critical for safety predictions.
3D & LiDAR Annotation for Autonomous Vehicles
Precise spatial annotation for 3D perception and sensor fusion
3D Bounding Box Annotation
Precise cuboid annotation around vehicles, pedestrians, cyclists, and obstacles in 3D space with accurate position, dimensions, and heading angle.
LiDAR Point Cloud Segmentation
Classify point clouds into semantic categories: road, vehicle, pedestrian, cyclist, vegetation, building, curb, guardrail, and free space.
3D Object Tracking
Maintain consistent object IDs across LiDAR sequences through occlusions, sensor gaps, and complex multi-object interactions.
Sensor Fusion Annotation
Synchronize annotations across LiDAR, camera, radar, and IMU sensors with perfect temporal and spatial alignment.
HD Map Annotation
Create and annotate high-definition maps with lane topology, road geometry, intersection structure, and traffic control devices.
Drivable Space Annotation
Label navigable areas, parking spaces, intersection zones, and obstacle-free regions for path planning and motion control.
Video & Temporal Annotation
Multi-Object Tracking (MOT)
Track vehicles, pedestrians, and cyclists across video frames with consistent IDs, handling occlusions and re-identification.
Trajectory & Motion Prediction
Annotate object trajectories, velocity vectors, and motion patterns for predictive modeling and collision avoidance.
Dynamic Scene Understanding
Label temporal events including vehicle maneuvers, pedestrian actions, and traffic flow patterns.
Behavioral Annotation
Classify driver behaviors, pedestrian intentions, and road user interactions for behavior prediction models.
Scenario & Event Annotation
Driving Scenario Classification
Categorize driving scenes by complexity: highway, urban, residential, parking, construction zones, adverse weather, night driving.
Critical Event Labeling
Identify and annotate safety-critical events including near-misses, aggressive maneuvers, emergency braking, and pedestrian conflicts.
Weather & Lighting Conditions
Label environmental conditions: sunny, rainy, foggy, snowy, dawn, dusk, night, headlight glare, and low-visibility scenarios.
Road Condition Annotation
Classify road surface conditions: dry, wet, icy, snowy, construction, potholes, speed bumps, and surface irregularities.
Automotive Domain Expertise
Unlike general annotation vendors, AI Taggers employs automotive-trained annotators who understand safety-critical requirements.
Traffic rules & regulations
Knowledge of traffic laws, right-of-way rules, and driving conventions across multiple countries and regions.
Vehicle dynamics
Understanding of vehicle behavior, turning radii, braking distances, and physical constraints affecting annotation decisions.
Vulnerable road user behavior
Recognition of pedestrian, cyclist, and motorcyclist behavior patterns critical for safety predictions.
Edge case recognition
Identification of unusual scenarios: construction zones, emergency vehicles, animal crossings, debris, and ambiguous situations.
Sensor characteristics
Understanding of LiDAR properties, camera limitations, radar signatures, and multi-sensor fusion requirements.
Safety-Critical Quality Standards
Autonomous vehicle perception requires zero-compromise quality assurance.
Multi-stage verification process
Every driving scene passes through annotator → automotive reviewer → safety QA auditor → final validation checkpoints.
100% human-verified annotations
Real experts validate every safety-critical object including pedestrians, cyclists, and vulnerable road users.
Edge case escalation protocols
Ambiguous or safety-critical scenarios reviewed by senior automotive annotators and domain experts.
Temporal coherence validation
Frame-by-frame tracking review ensures object IDs remain consistent and trajectories are physically plausible.
Safety-critical object prioritization
Enhanced QA focus on pedestrians, cyclists, children, and vulnerable road users with zero-tolerance for missed detections.
Scalability for AV Development
From pilot data to petabyte-scale datasets with safety-critical precision maintained at scale.
Driving scenes annotated
Objects per frame capacity
Global annotation teams
Safety object detection
Autonomous Vehicle Use Cases
Perception System Training
Train object detection, segmentation, and tracking models for camera-based, LiDAR-based, and sensor fusion perception stacks.
ADAS Feature Development
Build advanced driver assistance including adaptive cruise control, lane keeping, automatic emergency braking, and blind spot detection.
End-to-End Autonomous Driving
Develop full-stack autonomy from perception through planning and control for robotaxis, autonomous trucks, and shuttles.
HD Mapping
Create and maintain high-definition maps with lane-level accuracy for localization and route planning.
Simulation & Synthetic Data Validation
Annotate real-world data to validate synthetic datasets and assess sim-to-real transfer quality.
Safety Testing & Validation
Generate ground truth labels for testing perception system accuracy and validating corner cases.
Driving Scenarios We Annotate
Why AV Teams Choose AI Taggers
Automotive safety culture
Annotation processes designed with functional safety principles and ISO 26262 awareness.
Format flexibility
Deliver in KITTI, nuScenes, Waymo Open Dataset, A2D2, Argoverse, or your custom AV format.
Multi-sensor annotation
Synchronized labeling across camera arrays, LiDAR sensors, radar, GPS/IMU with frame-perfect alignment.
Geographic knowledge
Understanding of traffic regulations, signage standards, and driving conventions across deployment regions.
Transparent quality metrics
Track detection rates, tracking accuracy, annotation precision/recall, and safety-critical object coverage.
Sensor Configurations We Support
Autonomous Vehicle Annotation Process
Automotive Consultation
We review your sensor configuration, perception stack, deployment scenarios, and safety requirements. Our automotive experts develop annotation guidelines with your team.
Pilot Annotation
Annotate 100-500 representative driving scenes across diverse conditions. You evaluate quality against ground truth. We calibrate workflows based on feedback.
Production with Automotive QA
Distributed annotation teams process driving data with continuous automotive-specific quality monitoring. Weekly reports track safety-critical object accuracy.
Delivery with Safety Validation
Receive annotations with object IDs, 3D coordinates, tracking trajectories, and metadata. Safety-critical objects flagged and double-verified.
Real Results From AV Teams
"AI Taggers' safety-critical annotation quality exceeded our expectations—their zero-tolerance approach to missed pedestrians gave us confidence in our perception validation."
Perception Lead
Autonomous Vehicle Company
"The temporal tracking consistency across our driving sequences was flawless, even through challenging urban intersections with 50+ objects."
Technical Director
ADAS Development Firm
Get Started With Expert AV Annotation
Whether you're building robotaxis, developing ADAS features, or validating perception systems, AI Taggers delivers the safety-critical annotation quality your autonomous systems need.