Uber AI Solutions for automotive and autonomous vehicles
Make use of Uber's leading data labelling, real-world testing, and localisation to drive precision, safety, and scalability in automotive AI and autonomous vehicles
Why partner with Uber AI Solutions?
Uber’s end-to-end data labelling, advanced testing frameworks, and global scalability enable automotive and autonomous vehicle companies to achieve unmatched precision, safety, and operational efficiency. With over 8 years of experience and a global network of expert teams on the knowledge work marketplace, Uber helps drive innovation and accelerate deployment across autonomous and automotive AI applications.
High-precision data for robust model training
uLabel’s multisensor fusion supports advanced perception, enabling autonomous vehicles to understand and respond to complex environments.
Faster development and time to market
Streamlined data labelling and testing processes accelerate development cycles, supporting rapid deployment of new autonomous technologies.
Real-world validation and safety assurance
Scenario-based testing ensures that vehicles can handle unpredictable conditions, enhancing safety and reliability.
Operational efficiency and cost savings
Scalable solutions reduce overheads, enabling cost-effective growth and efficient resource allocation across projects.
Global adaptability through localisation expertise
Localisation and testing ensure autonomous systems meet region-specific needs, from regulatory requirements to local road customs.
Enhanced safety and compliance
Rigorous safety testing ensures that autonomous systems meet global safety standards and regulatory benchmarks, safeguarding users and stakeholders.
How this could apply to you
High-fidelity data annotation for autonomous model training
Label intricate sensor data to create comprehensive training sets for autonomous decision-making, from object detection to road segmentation.
Impact: Enables more accurate and reliable perception models, improving safety and situational awareness
Real-world and scenario-based testing
Test autonomous systems across varied environments and driving scenarios, ensuring robustness under real-world conditions.
Impact: Improves reliability and performance, reducing the risk of system failures in complex environments
Localisation and global adaptability
Ensures autonomous systems are optimised for diverse global markets, from urban streets to rural areas, with region-specific tuning.
Impact: Expands operational capabilities worldwide, supporting deployment in different regulatory and geographic contexts
Product testing for durability and scalability
Comprehensively test vehicle performance, system stability, and hardware–software integration for scalable deployment.
Impact: Ensures consistent performance across vehicle models, regions, and environmental conditions
How we do this with our tools
- High-precision annotation for autonomous systems
uLabel provides detailed labelling for complex data types—including LiDAR, radar, camera, and sensor inputs—which are essential for training autonomous vehicle models.
- Multisensor fusion and 3D labelling
Supports comprehensive labelling across visual, depth, and spatial data, providing a unified dataset for situational awareness and decision-making.
- Dynamic annotation criteria
Creates datasets that strengthen autonomous models by using tailored parameters for object detection, road signs, lane markings, and environmental features.
- Real-time quality assurance
Ensures the highest-quality labelled data—by using built-in accuracy checks and AI-augmented validation—for reliable model training.
- Global task coordination for large-scale projects
uTask orchestrates and monitors complex labelling and testing tasks across Uber’s specialised teams, optimising workflows for high-volume autonomous data needs.
- Specialised workforce allocation
Assigns tasks to annotators with automotive and autonomy expertise, ensuring accuracy in labelling objects, pedestrian behaviour, and dynamic scenarios.
- Comprehensive analytics and progress tracking
Provides live dashboards with insights into project status, quality metrics, and team performance, ensuring complete project transparency.
- Scalable and adaptable infrastructure
Scales to meet complex, multimodal data needs because it’s designed to handle the vast data requirements of autonomous vehicle training.
- Real-world scenario simulation
uTest replicates diverse driving conditions—from city traffic to motorway environments—testing autonomous systems under real-world pressures and edge cases.
- Localisation and environmental adaptability testing
Ensures seamless performance across different regions, taking into account local driving regulations, road layouts, and weather conditions.
- Scenario-based safety testing
Uses real data to validate safety-critical functions such as obstacle avoidance, emergency braking, and pedestrian detection in unpredictable scenarios.
- AI-enhanced safety and compliance
Identifies potential safety risks and optimises system performance using AI-driven insights that Uber’s full-time programme managers oversee, thereby reducing deployment risks and supporting regulatory compliance.