Uber Scaled Solutions for automotive and autonomous vehicles

uLabel offers detailed labeling for complex data types—including LiDAR, radar, camera, and sensor inputs—essential for training autonomous vehicle models.

Supports comprehensive labeling across visual, depth, and spatial data, providing a unified dataset for situational awareness and decision-making.

Creates datasets that strengthen autonomous models by using tailored parameters for object detection, road signs, lane markings, and environmental elements.

Ensures the highest-quality labeled data—by using built-in accuracy checks and AI-augmented validation—for reliable model training.

uTask orchestrates and monitors complex labeling and testing tasks across Uber’s specialized teams, optimizing workflows for high-volume autonomous data needs.

Assigns tasks to annotators with automotive and autonomy expertise, ensuring accuracy in labeling objects, pedestrian behavior, and dynamic scenarios.

Provides live dashboards with insights into project status, quality metrics, and team performance, ensuring full project transparency.

Scales to meet complex, multimodal data needs because it’s designed to handle the vast data requirements of autonomous vehicle training.

uTest replicates diverse driving conditions—from urban traffic to highway environments—testing autonomous systems under real-world pressures and edge cases.

Ensures seamless performance across different geographies, accounting for local driving regulations, road structures, and weather conditions.

Leverages real data to validate safety-critical functions like obstacle avoidance, emergency braking, and pedestrian detection in unpredictable scenarios.

Identifies potential safety risks and optimizes system performance using AI-driven insights that Uber’s full-time program managers oversee, thereby reducing deployment risks and supporting regulatory compliance.