Scale AI
Case Studies
Nuro Enhances Autonomous Vehicle Safety with Nucleus Object Autotag
Overview
Nuro Enhances Autonomous Vehicle Safety with Nucleus Object AutotagScale AI |
Analytics & Modeling - Machine Learning Cybersecurity & Privacy - Identity & Authentication Management | |
Education Transportation | |
Logistics & Transportation | |
Tamper Detection Virtual Training | |
Training | |
Operational Impact
With the help of Nucleus, Nuro has been able to tackle the ever-growing long tail of rare scenarios in their training data. The company's Perception Team now maintains an impressive dataset at a scale of over 500 million images. By holding the amount of labeled data fixed for supervised learning, they've been able to handle more and more edge cases, such as avoiding collisions with large birds. Nuro intends to continue to scale up the size of its dataset that it curates with Nucleus, preparing the perception team to target any new edge cases that may appear. Future training data will likely include changing environmental conditions and identifying actions, such as the hand-wave of a pedestrian. This has allowed Nuro to improve the safety and efficiency of their automated deliveries with fewer human interventions required. | |
Quantitative Benefit
Nuro was able to identify 1000+ images of pedestrians in unusual postures with Nucleus, compared to ~60 with their internal tool. | |
Nucleus helped Nuro identify 400+ images of animal cases, compared to ~50 with their internal tool. | |
With Nucleus, Nuro was able to identify 500+ images of occluded and backlit pedestrians, compared to 10-20 with their internal tool. | |