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Creating large volumes of training data without sacrificing quality.

Overview

Toyota Research Institute’s mission is to build a new approach to mobility and pioneering the technologies that drive its future.

As part of this mission and vision, one of TRI’s key research areas is automated driving, developing two different automated driving modes in parallel - Guardian and Chauffeur. Guardian monitors a human’s driving task, intervening only when necessary. Chauffeur mode, on the other hand, takes all responsibility for driving, leaving occupants purely as passengers.

The Problem

Large volumes of data, but limited ability to label this data

Led by Adrien Gaidon, TRI’s Machine Learning team found itself with large volumes of data, but limited ability to label this data.

The need for large volumes of annotated data was obvious, but with a commitment to safety being non-negotiable, the TRI team did not want to trade off quality for quantity.

To support the research teams, David Garber and Ashmi Wadhwani were tasked with managing the data annotation pipeline, data infrastructure and machine learning infrastructure.

Very quickly, they recognized the need for a labeling provider they could rely on long term to meet the ever-changing needs of TRI’s researchers.

“Imagine a future where mobility is truly for all. Regardless of physical faculties, age, or income, you can participate in this world because technology has progressed to enable you to move as you want.”

David Garber

Product Manager

TRI

The Solution

Scale’s Sensor Fusion Cuboids, Sensor Fusion Segmentation and Semantic Segmentation

TRI looked into various solutions for its data annotation problem, but made Scale its main provider. This close working relationship led to TRI participating in the private beta of Sensor Fusion Segmentation, a challenging annotation type in which every point in a 3D point cloud needs to be painted.

The Result

Leveraging latest annotations and features

TRI will continue to leverage Scale's latest annotations and features, such as Sensor Fusion Segmentation.

One of the things we love about Scale is the fact that we can fully label the world [...] to understand as much as possible, including scenarios we don’t foresee today.