Zoox, Inc.

Michael Haggblade

Kai Zhenyu Wang

Gowtham Garimella

Andrew Scott Crego

Gerrit Bagschik

Andraz Kavalar

Benjamin Isaac Mattinson

James William Vaisey Philbin

Bertrand Robert Douillard

Andres Guillermo Morales Morales

Hao Li

Ethan Petrick Dreyfuss

Greg Woelki

Techniques relating to training a model for detecting that a vehicle is likely to perform a cut-in maneuver are described. Computing device(s) can receive log data associated with vehicles in an environment and can detect an event in the log data, wherein an event corresponds to a cut-in maneuver performed by a vehicle. In an example, the computing device(s) can generate training data based at least in part on converting a portion of the log data that corresponds to the event into a top-down representation of the environment and inputting the training data into a model, wherein the model is trained to output an indication of whether another vehicle is likely to perform another cut-in maneuver.

Perpendicular cut-in training

Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

Disengagement prediction for vehicles

Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.

Top-down scene discrimination

Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.

Top-down scene generation

Techniques are disclosed for re-executing a data processing pipeline following a failure of at least one of its components. The techniques may include a syntax for defining a compute graph associated with the data processing pipeline and receiving such a compute graph in association with a specific data processing pipeline. The technique may include executing the data processing pipeline, determining that a component of the data processing pipeline failed, and determining a portion of the data processing pipeline to execute/re-execute based at least in part on dependencies defined by the data processing pipeline in association with the failed component. Re-executing the one or more components may comprise retrieving an output saved in association with a component upon which the failed component depends.

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El Dorado Hills, CA, United States of America

Michael Haggblade