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Enhancing Autonomous Trucking with Synthetic Data: A Kodiak Robotics Case Study
Overview
Enhancing Autonomous Trucking with Synthetic Data: A Kodiak Robotics Case StudyScale AI |
Cybersecurity & Privacy - Identity & Authentication Management Sensors - Autonomous Driving Sensors | |
Plastics Transportation | |
Logistics & Transportation | |
Autonomous Transport Systems Virtual Training | |
System Integration Training | |
Operational Impact
The use of synthetic data has significantly improved Kodiak's ability to handle edge cases in autonomous trucking. By centralizing all their data, including multiple labeling projects and raw, unlabeled data, into a single dataset, the team can quickly iterate on model experiments, query for specific attributes or metadata on the fly, and close the loop for a more end-to-end data and model management system. Going forward, the team can review both insights and model metrics in Nucleus to identify scenes with poor IoU (intersection over union) and curate subsets of data where their model wasn't performing well, in which additional synthetic data might be helpful. This approach has enhanced the robustness of Kodiak's autonomous trucking model, making it more reliable and efficient. | |