Road-user detection
Challenge
Traffic participants overlap and change appearance across weather and lighting.
Our approach
Label vehicles, pedestrians and cyclists with visibility attributes and frame-consistent identities.
Introducing advanced AI data pipelines for Automotive
Road perception data for safer driving systems
Road scenes, cabin cameras and synchronized vehicle sensors need labels that capture real operational detail. Engai prepares automotive datasets around your objects, scenes and review requirements.
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Automotive Data Annotation
Expert Labeling of Automotive
Data
Image & Video Annotation for Autonomous Vehicles and the Automotive Industry.
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completed projects
Autonomous Fleets & ADAS
Automotive · 3D Cuboid & Sensor Fusion
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annotated files
Sensor Fusion Precision
Engai provides professional sensor fusion and scene understanding annotation for autonomous vehicles. Our experienced in-house annotation teams ensure your machine learning and embodied AI systems for self-driving car projects go smoothly.
Our proprietary annotation platform features a full suite of annotation techniques that can be adapted for your specific needs. Our annotators are comfortable working with all types, and qualities of data. We can also collect data for you from legal, open-source repositories, or even create bespoke data with our in-house studio.
Automotive / Capabilities
Automotive Core Solutions
Carefully annotated data for automotive perception, automation and research.
Automotive label taxonomy
Define classes for vehicles, pedestrians and road boundaries, with visual examples and explicit boundary rules.
Capture-aware quality review
Review the effects of lighting, scale and occlusion in road scenes, cabin cameras and synchronized vehicle sensors.
Model-ready annotation data
Connect automotive labels to source identifiers, scene metadata and the export schema your model uses.
Annotation capabilities
Automotive Data Services
Road scenes, cabin cameras and synchronized vehicle sensors need labels that capture real operational detail. Engai prepares automotive datasets around your objects, scenes and review requirements.
Locate each visible object with an axis-aligned rectangle and a class label. Applied to vehicles, pedestrians and road boundaries.
Data Annotation for Automotive
Autonomous driving perception
Label road users, lane geometry and drivable space across varied roads, weather and lighting for vehicle perception training.
Vehicle quality inspection
Outline visible bodywork defects and component conditions using consistent image-level and region-level labels.
Driver monitoring datasets
Annotate observable gaze direction, head pose and cabin actions with clear visibility rules for driver-monitoring research.
Maintenance condition analysis
Organize component imagery by visible wear, damage and operating context to support inspection and maintenance models.
Automotive AI assistants
Curate vehicle-related instructions and reviewed answers for technical support, inspection and fleet workflows.
Client reviews
on 
Careful work on the small details
The sample batch helped us agree on how to handle overlapping leaves and partially visible crops.
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Having those edge cases discussed early would give our team a clearer baseline before scaling annotation.
Clear communication throughout
Our labeling requirements evolved as we reviewed the imagery. The feedback process felt straightforward.
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A shared set of examples and regular checkpoints would help keep the dataset consistent across batches.
A practical approach to field data
Field images are rarely perfect. We appreciated the attention to shadows, occlusion and changes in lighting.
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These are the details we would want a labeling partner to consider when preparing data for crop-monitoring models.
Automotive in practice
Annotation applications across automotive workflows.
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Road-user detection
THE CHALLENGE
Traffic participants overlap and change appearance across weather and lighting.
THE SOLUTION
Label vehicles, pedestrians and cyclists with visibility attributes and frame-consistent identities.
DATA DELIVERABLE
Reviewed bounding boxes data with a consistent label schema and documented edge cases.
Road-user detection
+15%
Throughput target
A project target for improving throughput with task-specific training data.
Sub-CM
Resolution target
Where calibrated source imagery supports fine spatial measurements.
40%
Efficiency target
A project target for reducing review effort through assisted labeling.
Deployment options
Build your workflow around the infrastructure your team uses.
On-Premises
Secure local processing for sensitive datasets.
Cloud
Highly scalable infrastructure powered by AWS/GCP.
Edge
Real-time inference optimized for on-device hardware.
Formats & platforms
ONNX
TensorRT
Core ML
OpenVINO
LiteRT
TorchScript
PaddlePaddle
IMX500
Qualcomm
Huawei
Available formats depend on your model, runtime and target hardware.
AI & ML Production Acceleration
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Discover how Engai's Data Pipeline and AI Infrastructure platform can make your organization AI-ready:
Automatically discover and map all datasets with contextualized inventory
Effortlessly manage ML lifecycle and address governance gaps
Drastically reduce deployment time by mitigating edge-case risks
Immediately detect and respond to model drift to minimize threat impact
Proactively apply zero-trust protection mechanisms for proprietary data
Live Infrastructure Telemetry
1.2BData Points
189Models
24Pipelines
3Alerts
TypeOriginOwnerStatus
Vision ModelGitHubDave DigsterHealthy
DatasetAWS S3Emily ThompsonSyncing
DeploymentGCPRobert BrownActive
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.