Training Data for Self-Driving Systems & ADAS
Professional data collection from real commercial drivers in authentic road conditions — edge cases, adverse weather, and complex scenarios that break AI models.
The Long Tail Problem
Overrepresented Easy Scenarios
Highway driving in clear weather is common. Production AV systems fail on construction zones, unpaved roads, wildlife, and emergency vehicles.
Inconsistent Sensor Quality
Dashcam footage varies in resolution and frame rate. Perception stacks need consistent, calibrated multi-sensor data sets.
No Vehicle Telemetry
Without synchronized CAN bus data (speed, steering, brake), models lack crucial ground-truth for behavioral decision-making.
Folstag solves this by deploying professional operators with calibrated multi-sensor rigs in targeted scenarios. Every capture is planned, every edge case is intentional, and every frame ships with synchronized telemetry through our Folstag Anno platform. Learn more about our data collection methodology.
Edge Cases & Critical Scenarios We Target
Autonomous vehicles don't fail on straightaways in sunshine. They fail in the messy, ambiguous, low-frequency situations that make up the long tail of real-world driving. Folstag specializes in deliberately capturing these scenarios with professional drivers who operate in them daily.
Adverse Weather
Blizzards, freezing rain, whiteouts, and intense sun glare. Authentic extremes from -40°C to +30°C.
Construction Zones
Temporary lane shifts, human flaggers, and contradictory signage in active work zones.
Rural & Unpaved Roads
Gravel roads, wildlife crossings, and shared single-lane highways with heavy equipment.
Complex Intersections
Unprotected lefts, multi-lane roundabouts, and emergency vehicle yielding scenarios.
Night & Low Visibility
Mixed urban lighting, zero-streetlight rural roads, and low-angle sun transitions.
Parking & Low Speed
Tight residential driveways, pedestrian-heavy transit hubs, and commercial lot navigation.
Multi-Sensor Capture
We configure multi-camera arrays (360° coverage), LiDAR systems (32-128 channel), thermal imaging, radar, and IMU/GNSS positioning — all synchronized and calibrated for your perception stack. Vehicle telemetry (CAN bus) is synced through Folstag Anno. See our services page for full sensor specifications.
Autonomous Vehicle Data Collection FAQ
What types of autonomous vehicle edge cases does Folstag capture?
We target the long-tail scenarios that cause production AV failures: construction zones, adverse weather, wildlife crossings, unpaved roads, and complex intersections. Every scenario is captured by professional commercial drivers using calibrated multi-sensor arrays.
How does Folstag's vehicle telemetry sync work for AV datasets?
Folstag Anno synchronizes CAN bus telemetry — speed, steering, throttle, brake, and GPS — directly into the annotation feed, time-aligned with every video frame. This gives ML pipelines simultaneous access to visual and operational context.
Can you capture data in extreme winter conditions for AV training?
Yes. Alberta provides natural winter extremes reaching -40°C with heavy snowfall, ice-covered roads, blizzards, and whiteout conditions. Our professional drivers operate in these conditions routinely, capturing authentic sensor data without climate simulation.
What is an Operational Design Domain (ODD) and why does it matter for AV training data?
An ODD defines the specific conditions under which an autonomous vehicle is designed to operate — including road types, weather, speed ranges, and geographic boundaries. Folstag captures data across diverse ODDs from highways to construction zones in conditions ranging from -40°C snowstorms to summer rain, ensuring your perception models are validated against every scenario in your target domain.
What data formats does Folstag support for AV ML pipelines?
We deliver in all major formats: KITTI, nuScenes, COCO, Pascal VOC, and custom schemas. Deliverables include synchronized 4K video, LiDAR point clouds, thermal imagery, and CAN bus data — all with frame-level timestamps and calibration matrices ready for PyTorch, TensorFlow, or proprietary pipelines.
Ready to Accelerate Your Autonomous Vehicle AI?
Tell us about your AV perception stack, your target ODD, and the edge cases holding your model back. We'll design a capture plan that closes the gap.
Schedule Your Consultation