Zoox, Inc.

Andrew Lewis King

Robert Edward Somers

Thomas Michael Flanagan

Arpan Silas Rajan

Jefferson Bradfield Packer

Marc Wimmershoff

Maxwell Ethan Mckenzie Yaron

Subasingha Shaminda Subasingha

James William Vaisey Philbin

Chuang Wang

Ralph Michael Kling

Turhan Karadeniz

Jacob Lee Askeland

Andreas Christian Reschka

Ryan Martin Cahoon

Yu Liu

Austin Hendrix

Sandra Ruiz

Nathaniel John Villaume

Techniques for image data protection using cyclic redundancy checks are disclosed herein. Some of the techniques may include, at a processor, receiving image data that includes multiple lines of pixel data. The processor may also determine at least a first hash value representing a first line of pixel data of the multiple lines of pixel data and a second hash value representing a second line of pixel data of the multiple lines of pixel data. The processor may also send the image data to a computing device that is configured to determine, based at least in part on the first hash value and the second hash value, whether the image data is corrupt.

Image data protection using data integrity checks

Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, CPU-usage associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined CPU-usage. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomalous event.

Safe system operation using CPU usage information

Techniques for capturing and recording processor events and scheduler data in a production system on a per processing resource basis are discussed herein. In some examples, a process metric collection component may be associated with the scheduler and the processing resource such that the process metric collection component can capture real time data associated with the processes or threads both executed by the processing resource and waiting to execute on the processing resource. The captured data may be used by the system to monitor operations.

Continuous tracing and metric collection system

A vehicle may include a primary system and a secondary system to validate operation of the primary system and to control the vehicle to avoid collisions. For example, the secondary system may receive multiple trajectories from the primary system, such as a primary trajectory and a secondary, contingent, trajectory associated with a deceleration or other maneuver. The secondary system may determine if a trajectory is associated with a potential collision, if the trajectory is consistent with a current or previous pose, if the trajectory is compatible with a capability of the vehicle, etc. The secondary system may select the primary trajectory if valid, the secondary trajectory if the primary trajectory is invalid, or another trajectory generated by the secondary system if the primary trajectory and the secondary trajectory are invalid. If no valid trajectory is determined, the vehicle may decelerate at a maximum rate.

Collision avoidance system with trajectory validation

Techniques for encoding image data are discussed. Image data containing multiple image frames can be received from an image sensor associated with a vehicle. A first image frame and a second image frame may be associated with a first data chunk. A first metadata associated with the first image frame can be identified. Additionally, a second metadata associated with the second image frame can be identified. The first data chunk can be created that includes the first image frame, the second image frame, the first metadata, and the second metadata. The first data chunk can be stored in a video container, where the video container can be indexed to access the first image frame with the first metadata or the second image frame with the second metadata.

System and method of video encoding with data chunk

Techniques for determining simulations to confirm programmatic logic are discussed herein. Such simulations may be used to identify errors in programmatic logic. As an example, a system may simulate an autonomous vehicle operating in an environment by setting various initialization parameters. Temporal logic, such as Linear Temporal Logic (LTL) and/or Signal Temporal Logic (STL) may be used to determine a numeric cost associated with how closely one or more policies are violated for each simulation of a group of simulations. Based on the costs computed, additional sets of simulations may be created using an evolutionary algorithm. Flaws in programmatic logic controlling the system may be identified based on the evolutionary algorithms and cost defined.

Simulation generation using temporal logic

A secondary system operates on a vehicle to avoid a collision when a problem occurs with a primary system. For example, the secondary system may operate independently from the primary system to take over control of the vehicle from the primary system when the secondary system detects a potential collision, when an error occurs with the primary system, and so on. In examples, the primary system may implement first techniques, such as Artificial Intelligence (AI) techniques, to understand an environment around the vehicle and/or instruct the vehicle to move within the environment. In examples, the secondary system may implement second techniques that are based on positioning, velocity, acceleration, etc. of the vehicle and/or objects around the vehicle.

Perception collision avoidance

Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, latency associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined latency. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomaly.

Safe system operation using latency determinations

A coordinated component interface control framework may deterministically reproduce behavior of a data processing pipeline. The framework may include a controller that controls input to, output from, and/or execution of a component of the pipeline. The framework may also tune performance of the pipeline and/or enable parallel processing of the pipeline, even across different machines, while preserving the ability to deterministically reproduce behavior of the pipeline.

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Los Altos, CA, United States of America

Andrew Lewis King