Zoox, Inc.

Maxwell Yaron

Ryan McMichael

Jesse Sol Levinson

James William Vaisey Philbin

Robert J Chen

Robert Nicholas Moor

Till Kroeger

This disclosure describes methods, apparatuses, and systems for network training and testing for evaluating hardware characteristics and for hardware selection. For example, a sensor can capture a dataset, which may be transformed into a plurality of modified datasets to simulate changes to hardware. Each of the plurality of modified datasets may be used to individually train an untrained neural network, thereby producing a plurality of trained neural networks. In order to evaluate the trained neural networks, each neural network can be used to ingest an evaluation dataset to perform a variety of tasks, such as identifying various objects within the dataset. A performance of each neural network can be determined and compared. A performance curve can be determined for each characteristic under review, facilitating a selection of one or more hardware components and/or configurations.

Network training process for hardware definition

Techniques for calibrating a camera utilizing a calibration station. The camera calibration station may be configured to emit collimated light toward a camera housed in a cradle. The camera may capture images of the collimated light at pre-determined positions throughout a calibration sequence. A computing system associated with the camera calibration station may utilize reference locations determined based on the collimated light at the pre-determined positions compared to measured locations of the collimated light at the pre-determined positions to determine intrinsics of the camera (e.g., focal length of lens, optical center of lens, etc.) and an error associated therewith. Based at least in part on the error being less than a threshold error, the computing system may store the intrinsic parameters of the camera.

Camera calibration system

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Palo Alto, CA, United States of America

Maxwell Yaron