Zoox, Inc.

Rasoul Kabirzadeh

Joseph Funke

Jacob Lee Askeland

David Evan Zlotnik

Ryan Martin Cahoon

Jacob Patrick Thalman

Bryan Matthew O'Malley

Shao-Han Tang

Vincent Andreas Laurense

Joshua Dean Egbert

Amirhossein Tamjidi

Alireza Ashayer

Yangwei Liu

Techniques are described herein for determining simulated vehicle positional errors and correlating the positional errors with vehicle features. Such techniques may include receiving log data comprising trajectories and position data for a real vehicle, and executing a log-based simulated vehicle based on the log data. A simulated vehicle may be controlled to follow a simulation trajectory in a simulated environment based on the trajectory of the real vehicle. A simulation system may determine a difference between the positions of the simulated vehicle and corresponding positions of the real vehicle in the real environment. The techniques may further include determining a vehicle state features correlated to the lateral and/or longitudinal position errors of the simulated vehicle, and determining, based on the vehicle state features, position error distributions and/or models that can be used to control subsequent driving simulations.

Simulated vehicle position errors in driving simulations

Techniques for instantiating objects in a simulated environment based on log data are disclosed herein. Some of the techniques may include receiving log data representing an environment in which a real-world vehicle was operating. Using the log data, a simulated environment for testing a simulated vehicle may be generated. The simulated environment may represent the environment in which the real-world vehicle was operating. The techniques may further include determining a location of the simulated vehicle as it traverses the simulated environment. Based at least in part on the log data, a prior location of the real-world vehicle in the environment closest to the location of the simulated vehicle in the simulated environment may be determined. In this way, the simulated environment may be updated to include a simulated object representing an object in the environment that was perceived by the vehicle from the prior location.

Instantiating objects in a simulated environment based on log data

Techniques associated with detecting non-deterministic behavior with a component and/or subcomponents of an autonomous vehicle are discussed herein. In some cases, a simulation system may be configured to simulate operations of the autonomous vehicle and to detect changes in behavior between instances and with respect to log data or expected results. The simulation system may flag or otherwise identify components and/or subcomponents responsive to detecting potentially non-deterministic behavior.

Verifying reproducibility for a vehicle controller

A vehicle can capture data for use in a simulator. Objects represented in the vehicle data can be instantiated in simulation and move according to object models/controllers. A user can tune how closely simulated motion of the object corresponds to the previously recorded data based on simulation costs. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within a simulated environment. The scenarios can include simulated objects that traverse the simulated environment and perform actions based on the captured data and/or interactions within the simulated environment. Objects observed by the vehicle can be disregarded from simulation based on one or more filters. A user can override, augment, or otherwise modify simulations instantiated based on the one or more filters and captured data.

Scenario editor and simulator

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Redwood, CA, United States of America

Rasoul Kabirzadeh