Zoox, Inc.

Daniel Miller

Derek Adams

Ryan McMichael

Joseph Patrick Warga

Patrick Blaes

Christopher William Labadie

Daniel Glenn Johnson

Anubhav Thakur

Austin In-Jei Yi

Peter Thomas Mitros

Jesse Sol Levinson

Aleksandrs Ecins

Francesco Papi

David Burdick Berman

Guillermo Duenas Arana

Richard Martin Murray

Soroush Dean Khadem

Anne-Claire Elisabeth Marie Le Henaff

Ryan Mcmichael

Techniques for efficiently generating systems are disclosed herein. Abstract system definitions can define the various functions of a system and/or the fitness requirements of the system. Components capable of implementing the aspects of the abstract system can be determined and systems configured to perform the functions of the abstract system are determined. The fitness of these systems can be evaluated to determine whether they meet the fitness requirements of the abstract system. If not, an algorithm is used to iteratively adjust the configurations and components of the systems until the fitness criteria is met.

Computational framework for automatic high assurance system design

Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.

Modifying map elements associated with map data

An evaluation computing system may implement techniques to validate a vehicle controller, such as based on an update thereto. The evaluation computing system may access data associated with an operation of the vehicle in an environment as controlled by the controller. The evaluation computing system may modify a portion of the data representative of a fault associated with the controller. The evaluation computing system may run a simulation utilizing modified log data to determine whether the controller detects and/or mitigates the fault within a threshold time. Based on a determination that the controller does not detect and/or mitigate the fault within the threshold time, the evaluation computing system may determine an error associated with the controller. Based on a determination that the controller detects and/or mitigates the fault within the threshold time, the evaluation computing system may validate the controller.

Fault detection in vehicle control systems

A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Sensor pod assembly

A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. The sensor pod system may include a cleaning system to clean sensing surfaces of sensor pods during operation. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Sensor pod cleaning system

This disclosure is directed to calibrating sensors for an autonomous vehicle. First sensor data and second sensor data can be captured by one or more sensors representing an environment. The first sensor data and the second sensor data can be associated with a grid in a plurality of combinations to generate a plurality of projected data. A number of data points of the projected data occupying a cell of the grid can be summed to determine a spatial histogram. An amount of error (such as an entropy value) can be determined for each of the projected data, and the projected data corresponding to the lowest entropy value can be selected as representing a calibrated configuration of the one or more sensors. Calibration data associated with the lowest entropy value can be determined and used to calibrate the one or more sensors, respectively.

Sensor calibration

A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly disposed on a vehicle, for example, an autonomous vehicle to collect and provide data of the environment during operation of the vehicle. The sensor pods may be disposed at elevated locations around the vehicle to reduce obstacles within the sensor pods fields of view.

Sensor pod coverage and placement on vehicle

A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle. A combination of techniques may be used to calibrate a sensor pod prior to installation in the vehicle and may be used to determine if the sensor pod is compatible with other sensor pods and may also be used to calibrate other sensors when integrating the sensor pod into the vehicle.

Sensor pod calibration

A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may include a housing and extend from a portion of a body of a vehicle. The sensor pod housing may have energy absorbing structures configured to absorb and dissipate energy during an impact in order to protect a pedestrian. The sensor pod may have deformable portions of the housing configured to absorb and dissipate energy during the impact. The sensor pod may have deformable fasteners coupling a sensor to the sensor pod configured to deform to absorb and dissipate energy during the impact.

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San Jose, CA, United States of America

Daniel Miller