Built for the Long Tail Your AV Stack Will Meet
AV perception and planning systems must be robust enough to handle all variables: rare weather, occlusion, pedestrian behavior, and how these sensor data could conflict. Chemin provides a full suite of solutions that address these edge cases, to ensure your model is always moving forward.
Sensor Fusion
Align camera, LiDAR, radar, and IMU data through accurate synchronization and calibration to prevent training errors.
LiDAR and Camera Annotation
Label only what each sensor captures, accounting for distance, weather, glare, and low-light limitations.
Quality Assurance
Maintain consistent 3D boxes and pixel-level segmentation to prevent annotation drift across large datasets.
Continuous Improvement
Feed validation failures and new edge cases back into the dataset to keep perception models accurate over time. change.
The challenges that hold back AV data programs
Sensor Fusion and Calibration
Cameras, LiDAR, radar, and IMU each have their own field of view and failure mode, and a small calibration error degrades everything trained on top of it.
Labeling What Each Sensor Sees
A LiDAR cloud goes sparse at range and a camera loses objects in glare, so annotators need real judgment rather than just a labeling tool.
Grading 3D and Pixel-Level QA
Bounding boxes and segmentation are harder to grade for consistency than a class label, and small disagreements compound across a large dataset.
Data generation from source to success
End-to-end data services from sourcing and annotation to data generation for model training
100M+
30,000+
18
Data generation from source to success
End-to-end data services from sourcing and annotation to data generation for model training
100M+
30,000+
18
Frequently asked questions
Find answers to common questions about our services, process, and how we can support your needs.
Which sensor modalities can Chemin annotate for AV programs?
LiDAR, camera, radar, and IMU data, labeled together so outputs stay synchronized across modalities.
Can Chemin support simulation-based validation for planning models?
Yes, through RLVR: task and verifier design for simulation based robotics and planning tasks with checkable outcomes.
Can Chemin annotate data from multiple sensor configurations?
Yes. Different autonomous vehicle programs use different combinations of cameras, LiDAR, radar, and other sensors. Annotation workflows are adapted to your sensor stack while maintaining alignment across synchronized data streams.
Can Chemin help identify edge cases for model improvement?
Yes. We support targeted edge-case identification based on validation results and model performance, helping teams prioritize the scenarios most likely to improve future model iterations.
How does Chemin maintain annotation consistency across large projects?
Annotation quality is maintained through task-specific rubrics, reviewer calibration, gold standard QA, and ongoing measurement of inter-annotator agreement. These processes help ensure datasets remain consistent as projects scale.
Can Chemin support ongoing autonomous vehicle programs?
Yes. Many autonomous vehicle datasets continue evolving throughout development. We support ongoing annotation, validation, and dataset refinement as new scenarios, edge cases, and model requirements emerge.
What kinds of perception tasks does Chemin support?
Our teams support a range of perception workflows, including object detection, semantic segmentation, instance segmentation, object tracking, lane annotation, and multi-sensor perception tasks tailored to your program.
Can Chemin support validation datasets as well as training datasets?
Yes. We support both training and validation data. Evaluation datasets are designed to reflect real-world operating conditions and help engineering teams measure model performance across successive development cycles.
Trust the labels behind every sensor
Get a pilot dataset built around your sensor stack and task, reviewed by experts before it ever reaches training.