Zoox, Inc.

Nehemia Girma Terefe

Collin MacGregor

Samantha Marie Ting

Holly Ho

Mohammad Umar Piracha

Xuan Zhong

Aaron Rosekind

He Yi

Sandra Ruiz

Alejandro Ruiz

Darrin Sammons Willis

David Donald Logan

Nathaniel John Villaume

Timothy Henry Jung

Mohammed Umar Piracha

Matthew Ambrose Romlewski

Joshua Paul Munic

Janakan Chandrakumar

Particulate matter, such as fog, snow, rain, steam, vehicle exhaust, debris (plastic bags), etc. may cause one or more sensor types to generate false positive solid surface detections. In particular, various depth measurements may be impeded by particulate matter. Identifying false positive return(s) may comprise clustering lidar points, determining differences in range indicated by two different lidar devices having lidar points in the cluster, determining first differences that are more negative than a negative difference threshold and second differences that are more positive than a positive difference threshold, determining a first portion of lidar data in the cluster associated with the first differences and the second differences is associated with particulate matter or debris, and controlling a vehicle based at least in part on suppressing the first portion of the lidar data or indicating that the first portion of the lidar data is associated with particulate matter or debris.

Lidar sensor denoising for adverse conditions and/or nonsalient objects

A method includes receiving an audio signal from a microphone and processing the audio signal to identify a signal component that is associated with a resonance of at least part of a microphone unit disposed on a vehicle. The microphone unit includes the microphone and a protective element arranged to shield the microphone from environmental material. The method includes determining, based at least in part on the identified signal components, a characteristic indicative of the protective element having shielded the microphone from environmental material.

Rain detection using exterior microphones

This disclosure relates to detecting mirrored lidar returns and modifying lidar data using clustering techniques. In some examples, lidar data including a set of lidar points may be captured by a vehicle operating in an environment. The vehicle may combine the lidar data from numerous lidar devices to have a common frame of reference. In some examples, the vehicle may determine a cluster of the lidar points based on elevation data and azimuth data. The vehicle may compare range data of the clusters to determine whether such lidar points are indicative of a lidar return associated with an error (including multipath reflections). Based on determining that the difference in ranges of two lidar points is above a threshold value, the vehicle may determine how to use the data.

Mirror object detection based on clustering of sensor data

Techniques are discussed herein for detecting and managing faults occurring within various vehicle systems, to enable the vehicle to respond safely and appropriately to the faults. In examples, a constraint generator may receive sensor data associated with the operation of a vehicle in an environment. The constraint generator may perturb the sensor data to mimic particular sensor faults, and execute simulations to determine performance degradations of the vehicle systems associated with the particular sensor faults. Based on the performance degradations associated with sensor faults, the constraint generator may determine the appropriate constraints with limitations on the operation of the vehicle, and communicate the constraint parameters to the appropriate vehicle systems accordingly.

Vehicle constraint generation

Techniques for identifying when a sensor of a vehicle is obstructed. For instance, the sensor may include a light source (e.g., a light-emitting diode), a main sensor, a lens, a window, and/or a light sensor. The light source of the sensor may emit light, where the main sensor and/or the light sensor generate sensor data representing at least a portion of the light that is reflected. The vehicle may then determine a characteristic associated with the at least the portion of the light, such as an area of illumination or an intensity of the at least the portion of the light. Next, the vehicle may compare the characteristic to a threshold in order to determine whether the characteristic satisfies the threshold. Based on the determination, the vehicle may determine whether the sensor is obstructed, such as by a substance being located on the lens and/or the window.

Techniques for identifying vehicle sensor degradation

Techniques for detecting an obstruction associated with a lidar sensor are discussed herein. For example, a computing device can implement an obstruction detection component to detect rain, mud, dirt, dust, snow, ice, animal droppings, etc., on and/or near an outer surface of the lidar sensor. The obstruction detection component can apply one or more heuristics and/or models to the lidar data and/or compare pulse information associated with the lidar data to a threshold to determine a size, a type, or a location of an obstruction blocking a lidar beam.

Lidar obstruction detection

Apparatus and methods for determining an atmospheric condition in an environment of a vehicle are described herein. A long wave infrared camera may be used to produce thermal image data from a field of view of the LWIR camera that includes an unknown atmospheric condition. The thermal image data may include a characteristic that may be compared to characteristic of thermal image data for a known atmospheric condition. A result of the comparison may be used to make a determination related to the unknown atmospheric condition which may be used in controlling the vehicle.

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Foster City, CA, United States of America

Nehemia Girma Terefe