Autonomous Driving Datasets for Vehicle AI Training
Multi-sensor autonomous driving datasets with LiDAR, camera, and radar fusion. Geographically diverse, weather-balanced, and edge-case rich for production AV systems.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Open AV Benchmarks
Waymo Open, nuScenes, and KITTI enabled early AV research. Production autonomous systems require far more.
Autonomous vehicles must operate safely across climates, geographies, and infrastructure types that benchmark datasets never covered. Real deployment failures often originate from distribution shift: conditions the model has never seen.
Public AV datasets suffer from geographic concentration (US and European roads dominate), sensor lock-in (configured for specific hardware), and rare scenario scarcity for the long-tail events that cause accidents.
LXT builds custom autonomous driving datasets engineered to close the distribution gap. We collect across your target ODD (Operational Design Domain), with multi-sensor configurations, targeted adverse condition capture, and the rare scenarios your safety team needs to validate.
Why Teams Upgrade
Limitations of Public Autonomous Driving Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| Waymo Open Dataset | Primarily San Francisco and Phoenix; limited to Waymo's proprietary sensor stack | Geographic bias |
| nuScenes | Singapore and Boston only; 1,000 scenes insufficient for rare condition training | Scale limits |
| KITTI | Karlsruhe city only; 2012 hardware; no adverse weather or night scenes | Outdated scope |
| Lyft Level 5 | Palo Alto routes only; discontinued with no ongoing updates | Narrow ODD |
| ApolloScape | Chinese urban scenes; limited international transferability and sensor diversity | Domain narrow |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Sensor Configuration
Hardware Coverage
- LiDAR: Single and multi-beam configurations from Velodyne, Ouster, and Hesai
- Camera Arrays: Surround-view, stereo, and forward-facing setups with calibration data
- Radar and GNSS: Radar point clouds and high-precision GPS/IMU for trajectory data
Operational Design Domain
ODD Coverage
- Geography: Urban, suburban, rural, highway, and mixed-use environments
- Weather: Rain, snow, fog, direct sun, and low-light nighttime conditions
- Geography Diversity: Target-country roads, infrastructure, and traffic patterns
Annotation Depth
Perception Annotations
- 3D Bounding Boxes: Per-object 3D boxes with class, velocity, and orientation
- Semantic and Panoptic Segmentation: Full scene pixel and point labeling
- HD Map Elements: Lane lines, road boundaries, traffic signs, and signal states
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Long-Tail and Safety-Critical Scenarios
High-accuracy models handle rare attributes that public datasets miss.
Adverse Weather Degradation
Sensor performance under rain, snow, and fog. LiDAR and camera degradation modes that challenge perception stacks in real deployment conditions.
Construction Zones and Irregular Infrastructure
Temporary lane markings, absent road boundaries, and unconventional intersections that confuse map-based systems.
Vulnerable Road User Interactions
Pedestrians, cyclists, and scooters in unexpected positions and trajectories, particularly at night and in poor visibility.
Near-Miss and Pre-Collision Scenarios
Close-call scenarios that require controlled simulation with safety protocols. Essential for safety-critical validation and regulatory approval.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
3D Bounding Box Annotation
Frame-accurate 3D boxes in LiDAR and camera space with class, tracking ID, velocity estimate, and occlusion rating.
HD Map and Lane Annotation
Lane line topology, road boundary markings, traffic sign positions, and signal states for map-based planning.
Semantic and Panoptic Segmentation
Per-pixel and per-point class labels for camera images and LiDAR point clouds aligned across sensor modalities.
Industry Applications
Autonomous Driving Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Self-Driving Cars
Full autonomy perception and planning
HD Mapping
Road network and infrastructure modeling
Safety Validation
Scenario-based testing and regulatory approval
ADAS Systems
Level 2 and Level 3 driver assistance
Commercial Trucking
Highway and logistics route automation
Simulation
Synthetic data generation and domain randomization
Global ODD Expansion
New geography rollout datasets
Industrial Vehicles
Forklifts, mining, and port automation
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Autonomous Driving Dataset FAQs
What sensor configurations do you support?+
We work with Velodyne, Ouster, Hesai, and Luminar LiDARs combined with camera arrays from 1 to 12 cameras. Radar and IMU/GNSS integration is available. We can match your production sensor stack exactly.
Can you collect in specific countries or road types?+
Yes. We have established collection capabilities across North America, Europe, Asia, the Middle East, and Africa. We target specific road types (urban, highway, rural), infrastructure types, and traffic density conditions per your ODD spec.
How do you handle adverse weather collection?+
We schedule collection campaigns in target weather windows, partner with regions that experience specific conditions year-round, and use controlled simulation for rare events like black ice or extreme fog. All simulated scenarios are clearly labeled.
What annotation formats do you deliver?+
nuScenes JSON, KITTI bin, Waymo TFRecord, and custom formats. For camera data, COCO JSON and custom formats. Full calibration matrices and synchronization metadata included.
How do you capture rare scenarios?+
For genuine rare events, we use long-duration naturalistic driving campaigns. For safety-critical scenarios (near-collisions, cyclist cut-ins), we use closed-track controlled scenarios with trained performers.
What does a custom AV dataset cost?+
Projects range from $50K for targeted scenario collections to $500K+ for large-scale multi-geography, multi-sensor campaigns. We provide detailed quotes after reviewing your ODD specification.
Get Started
Scope Your Custom Autonomous Driving Dataset
Share your ODD specification, sensor configuration, and target scenarios. Our automotive data specialists will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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