Zoox, Inc.

Harrison Thomas Waschura

Subasingha Shaminda Subasingha

Turhan Karadeniz

Joseph Patrick Warga

Robert Nicholas Moor

Silas Kogure Wilkinson

Mehran Ferdowsi

Yongzhe Chen

Samuel Holladay

Ryan McMichael

Denis Nikitin

Minh Nguyen

Techniques for determining a distance to an object in an environment based on sensor data are discussed herein. The sensor data can be captured by a sensor, and the sensor data can be based on laser pulses emitted from emitters being differently spaced apart from the sensor. The sensor data can be utilized to determine distance data associated with an object. A difference between the distance data can be used to determine whether the distance data is associated with a retroreflector. If the difference in distance data is above a threshold, a confidence level of sensor data can be adjusted or the sensor data can be discarded or omitted from subsequent processing.

Glare detection in sensor data

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. An estimated depth of an object may be determined from the first image data, and an actual depth of the object may be determined from the second image data, based on the estimated depth. In examples, the first and second configurations have different modulation frequencies such that a nominal maximum depth in the first configuration is greater than the nominal maximum depth in the second configuration.

Determining depth using multiple modulation frequencies

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can be impacted by glare. In some examples, an emitter system for illuminating a field of view can be dynamically altered to provide different illumination intensities at different regions in the field of view. For instance, locations at which a highly-reflective object or other object that is likely to cause glare may be illuminated at a lower illumination intensity, e.g., to reduce the likelihood of pixel saturation in measured data associated with the object.

Dynamic sensor illumination

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.

Depth dependent pixel filtering

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. A disambiguated depth of a surface may be determined from the first image data and the second image data. If the disambiguated depth is greater than a nominal maximum depth of the sensor in the first configuration, an intensity of the surface may be determined from the first image data. If the intensity meets or exceeds a threshold intensity, the surface may be determined to be beyond the nominal maximum depth. If the intensity is less than the threshold intensity, an actual depth of the surface may be determined form the second image data as a distance less than the nominal maximum depth.

Determining pixels beyond nominal maximum sensor depth

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over-exposure or saturation. In some examples, multiple exposures captured at different exposure times can be used to determine an overall saturation value or metric representative of the sensor data. The saturation value may be used to control parameters of the sensor. For instance, the saturation value may be used to determine power control parameters for the sensor, e.g., to reduce over- and/or under-exposure.

Power control of sensors using multiple exposures

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include saturated pixels, e.g., due to over-exposure, sensing highly-reflective objects, and/or excessive ambient light. In some examples, parameters associated with power of a time-of-flight sensor can be altered based on characteristics of the saturated pixels, as well as information about non-saturated pixels neighboring the saturated pixels. For example, the neighboring pixels may provide information about whether saturation is due to ambient light, e.g., sunlight, or due to emitted light from the sensor.

Active power control using neighboring pixels

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return pixels subject to over-exposure or saturation, which may be from stray light. In some examples, multiple exposures captured at different exposure times can be used to determine a saturation value for sensor data. The saturation value may be used to determine a threshold intensity against which intensity values of a primary exposure are compared. A filtered data set can be obtained based on the comparison.

Pixel filtering using multiple exposures

Techniques for facilitating a robust clock synchronization across a computer network that presumes network jitter exists are discussed herein. A first device and a second device transceive a plurality of sets of time-synchronization messages to synchronize a synchronization clock of the second device to a first clock of the first device. The second device calculates a smoothing of time delay data of a plurality of sets. The time delay data is associated with a transmission duration of time-synchronization messages of the sets of the plurality. The second device sets a synchronization clock based on a time at the first device and the smoothed time delay data.

Robust clock synchronization over computer network

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can be impacted by glare. In some examples, corrected data is generated by quantifying glare. A glare region including pixels that are not associated with an object in a range of the time-of-flight sensor may provide glare intensity and glare depth values used to quantify the glare. The glare intensity and glare depth may be used to correct measured data.

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Los Altos Hills, CA, United States of America

Harrison Thomas Waschura