Zoox, Inc.

James William Vaisey Philbin

Sarah Tariq

Kai Zhenyu Wang

Chuang Wang

Kratarth Goel

Zeng Wang

David Pfeiffer

Andrew Scott Crego

Marc Wimmershoff

Vasiliy Karasev

Cooper Stokes Sloan

Tencia Lee

Ashutosh Gajanan Rege

Andreas Christian Reschka

Yi Xu

Benjamin John Sapp

Gerrit Bagschik

Antoine Ghislain Deux

Rodin Lyasoff

Gabriel Thurston Sibley

Nitesh Shroff

Janek Hudecek

Josh Alexander Jimenez

Feng Tian

Yuanyuan Chen

Mahsa Ghafarianzadeh

Praveen Srinivasan

Sai Anurag Modalavalasa

Noureldin Ehab Hendy

Adriano Di Pietro

Juhana Kangaspunta

Arsalan Mousavian

Bryce A Evans

Xi Joey Hong

Tianyi Cai

Pengfei Duan

Nicholas George Charchut

Jesse Sol Levinson

Subhasis Das

Ryan McMichael

Robert J Chen

Bertrand Robert Douillard

Joshua Kriser Cohen

Andrew Lewis King

Benjamin Isaac Zwiebel

Michael Haggblade

Jacob Daniel Boydston

Robert Evan Mahieu

Yuesong Xie

Kai Yu

Maxwell Yaron

Yilun Wang

Maxwell Ethan Mckenzie Yaron

Hang Ren

Yuan Zhang

Greg Woelki

A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

In-path obstacle detection and avoidance system

Techniques are discussed for determining prediction probabilities of an object based on a top-down representation of an environment. Data representing objects in an environment can be captured. Aspects of the environment can be represented as map data. A multi-channel image representing a top-down view of object(s) in the environment can be generated based on the data representing the objects and map data. The multi-channel image can be used to train a machine learned model by minimizing an error between predictions from the machine learned model and a captured trajectory associated with the object. Once trained, the machine learned model can be used to generate prediction probabilities of objects in an environment, and the vehicle can be controlled based on such prediction probabilities.

Trajectory prediction on top-down scenes and associated model

Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

Map consistency checker

Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object.

Top-down view object detection and tracking

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.

Collision avoidance perception system

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 receiving and processing sensor data captured by a fleet of vehicle are discussed herein. In some examples, a fleet dashcam system can receive sensor data captured by electronic devices on a fleet of vehicles and can use that data to detect collision and near-collision events. The data of the collision or near-collision event can be used to determine a simulation scenario and a response of an autonomous vehicle control to the simulation scenario and/or it can be used to create a collision heat map to aid in operation of an autonomous vehicle.

Fleet dashcam system for autonomous vehicle operation

A machine-learning (ML) architecture for determining three or more outputs, such as a two and/or three-dimensional region of interest, semantic segmentation, direction logits, depth data, and/or instance segmentation associated with an object in an image. The ML architecture may output these outputs at a rate of 30 or more frames per second on consumer grade hardware.

Multi-task learning for real-time semantic and/or depth aware instance segmentation and/or three-dimensional object bounding

Energy consumption for a vehicle may be reduced based at least in part on an environment characteristic associated with the environment through which the vehicle travels or an operation characteristic associated with operation of the vehicle, thereby increasing an operational time of the vehicle. In some situations, reducing energy consumption may be associated with operation of one or more of a sensor (e.g., turning the sensor off, reducing a frequency or resolution of the sensor, etc.) and/or one or more processors associated with the vehicle (e.g., turning a processor off, reducing a rate of computation, etc.) based at least in part on one or more of the environment characteristic signal or the operation characteristic signal.

Energy consumption control systems and methods for vehicles

Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.

Perception error models

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may comprise one or more machine learned models, each of which may be trained to output one or more occupancy maps based at least in part on sensor data of different types. The occupancy maps may include a prediction of whether at least a portion of an environment is occupied at a future time by any one of multiple object types. Occupancy maps associated with a same time may be aggregated into a data structure that may be used to validate, reject, or replace the trajectory.

Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.

Safety analysis framework

Using detection of low variance regions for improving detection is described. In an example, sensor data can be received from a sensor associated with a vehicle. The sensor data can represent an environment. An indication of a low variance region associated with the sensor data can be determined and an indication of a high variance region associated with the sensor data can be determined based at least in part on the indication of the low variance region. The vehicle can be controlled based on at least one of the sensor data or the indication of the high variance region.

Low variance region detection for improved high variance region detection using machine learning

Low variance detection training is described herein. In an example, annotated data can be determined based on sensor data received from a sensor associated with a vehicle. The annotated data can comprise an annotated low variance region and/or an annotated high variance region. The sensor data can be input into a model, and the model can determine an output comprising a high variance output and a low variance output. In an example, a difference between the annotated data and the output can be determined and one or more parameters associated with the model can be altered based at least in part on the difference. The model can be transmitted to a vehicle configured to be controlled by another output of the model.

Training a machine-learned model to detect low variance regions

Techniques for utilizing multiple scales of images as input to machine learning (ML) models are discussed herein. Operations can include providing an image associated with a first scale to a first ML model. An output of the first ML model can include a first bounding box indicative of a first region of the image representing a first object, with the first bounding box falling within a first range of sizes. Next, a scaled image can be generated by scaling the image. The scaled image can be provided to a second ML model, which can output a second bounding box indicative of a second region of the image representing a second object, the second bounding falling within a second range of sizes. Thus, inputting a scaled image to a same ML model (or to different ML models) can result in different detected features in the images.

Restricted multi-scale inference for machine learning

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Palo Alto, CA, United States of America

James William Vaisey Philbin