Abstract: 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.

Abstract: Techniques for determining a probability of a false negative associated with a location of an environment are discussed herein. Data from a sensor, such as a radar sensor, can be received that includes point cloud data, which includes first and second data points. The first data point has a first attribute and the second data point has a second attribute. A difference between the first and second attributes is determined such that a frequency distribution may be determined. The frequency distribution may then be used to determine a distribution function, which allows for the determination of a resolution function that is associated with the sensor. The resolution function may then be used to determine a probability of a false negative at a location in an environment. The probability can be used to control a vehicle in a safe and reliable manner.

Abstract: This disclosure relates to systems and techniques for identifying collisions, such as relatively low energy impact collisions involving an autonomous vehicle. Sensor data from a first sensor modality in a first array may be used to determine a first estimated location of impact and second sensor data from a second sensor modality in a second array may be used to determine a second estimated location of impact. A low energy impact event may be configured when the first estimated location of impact corresponds to the second estimated location of impact.

Abstract: Particulate matter, such as dust, steam, smoke, rain, etc. may cause one or more sensor types to generate false positive detections. In particular, various depth measurements may be impeded by particulate matter. Identifying a false return and/or removing a false detection based at least in part on a sensor output may comprise determining a similarity of a portion of a return signal to an emitted light pulse or an expected return signal, determining a variance of the signal portion over time, determining a difference between a power spectrum of the return relative to an expected power spectrum, and/or determining that a duration associated with the signal portion meets or exceeds a threshold duration.

Abstract: Immersive experiences for users are described herein. In an example, audio data from a plurality of audio sensors associated with a vehicle can be received by an audio data processing system. The audio data processing system can combine individual captured audio channels (e.g., from the plurality of audio sensors) into two or more audio channels for output via two or more speakers proximate a user. A first audio channel of the two or more audio channels can be output via a first speaker and second audio channel of the two or more audio channels to be output via a second speaker, wherein output of the first audio channel and the second audio channel causes a resulting sound corresponding to at least a portion of a sound scene associated with the vehicle. In an example, a user computing device operable by the user can receive an input from the user.

Abstract: Techniques for adaptive cross-correlation are discussed. A first signal is received from a first audio sensor associated with a vehicle and a second signal is received from a second audio sensor associated with the vehicle. Techniques may include determining, based at least in part on the first signal, a first transformed signal in a frequency domain. Additionally, the techniques include determining, based at least in part on the second signal, a second transformed signal in the frequency domain. A parameter can be determined based at least in part on a characteristic associated with at least one of the vehicle, an environment proximate the vehicle, or one or more of the first or second signal. Cross-correlation data can be determined based at least in part on one or more of the first transformed signal, the second transformed signal, or the parameter.

Abstract: Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for estimating a direction of arrival of sound in an environment are discussed. First and second audio data are received from a first pair of audio sensors associated with a vehicle. A first region of ambiguity associated with the first pair of audio sensors is determined based on the first and second audio data. Third and fourth audio data are received from a second pair of audio sensors. A second region of ambiguity associated with the second pair of audio sensors is determined based on the third and fourth audio data. The regions of ambiguity can be further based on confidence levels associated with sensor or audio data. An area of intersection of the first region of ambiguity and the second region of ambiguity can be determined. A direction of arrival of an audio event can be determined based on the area of intersection.

Abstract: Techniques for determining a direction of arrival of an emergency are discussed. A plurality of audio sensors of a vehicle can receive audio data associated with the vehicle. An audio sensor pair can be selected from the plurality of audio sensors to generate audio data representing sound in an environment of the vehicle. An angular spectrum associated with the audio sensor pair can be determined based on the audio data. A feature associated with the audio data can be determined based on the angular spectrum and/or the audio data itself. A direction of arrival (DoA) value associated with the sound may be determined based on the feature using a machine learned model. An emergency sound (e.g., a siren) can be detected in the audio data and a direction associated with the emergency relative to the vehicle can be determined based on the feature and the DoA value.

Abstract: Techniques for using beam-formed acoustic notifications for pedestrian notification are described. Computing device(s) can receive sensor data associated with an object in an environment of a vehicle. The computing device(s) can determine first data for emitting a first beam of acoustic energy via speaker(s) of an acoustic array associated with the vehicle, and second data for emitting a second beam of acoustic energy via speakers of the acoustic array. The computing device(s) can cause the speaker(s) to emit the first beam in a direction of the object at a first time and the second beam in the direction of the object at a second time. Directions of propagation of the first beam and the second beam are offset so that the object can localize the source the acoustic notification, thereby localizing the vehicle in the environment.

Abstract: Techniques are described for determining a likelihood that a radar device failed to detect an object (i.e., a false negative). Determining the likelihood may be based at least in part on determining an estimated noise floor based at least in part on at least a portion of radar data, which may comprise one or more detections, and determining a likelihood that the portion of radar data includes a false positive, based at least in part on the estimated noise floor and a response profile associated with an object. A response profile may identify a received signal power and/or radar cross section associated with an object type.

Abstract: 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 noise. In some examples, the sensor data is filtered by comparing a measured intensity value to a threshold intensity value. The threshold intensity value can be determined on a per-pixel basis using depth information and/or intensity information for a corresponding image frame.

Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data and map data or perception data are discussed herein. A vehicle can use map data and/or perception data to distinguish between multiple audio signals or sounds. A direct source of sound can be distinguished from a reflected source of sound by determining a direction of arrival of sounds and which objects the directions of arrival are associated with in the environment. A reflected sound can be received without receiving a direct sound. Based on the reflected sound and map data or perception data, characteristics of sound in an occluded region of the environment may be determined and used to control the vehicle.

Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data and map data or perception data are discussed herein. A vehicle can use map data and/or perception data to distinguish between multiple audio signals or sounds. A direct source of sound can be distinguished from a reflected source of sound by determining a direction of arrival of sounds and which objects the directions of arrival are associated with in the environment. A reflected sound can be received without receiving a direct sound. Based on the reflected sound and map data or perception data, characteristics of sound in an occluded region of the environment may be determined and used to control the vehicle.

Abstract: A time delay of arrival (TDOA) between a time that a light pulse was emitted to a time that a pulse reflected off an object was received at a light sensor may be determined for saturated signals by using an edge of the saturated signal, rather than a peak of the signal, for the TDOA calculation. The edge of the saturated signal may be accurately estimated by fitting a first polynomial curve to data points of the saturated signal, defining an intermediate magnitude threshold based on the polynomial curve, fitting a second polynomial curve to data points near an intersection of the first polynomial curve and the intermediate threshold, and identifying an intersection of the second polynomial curve and the intermediate threshold as the rising edge of the saturated signal.

Abstract: A LIDAR sensor assembly includes a laser light source to emit laser light, and a light sensor to produce a light signal in response to sensing reflections of the laser light emitted by the laser light source from a reference surface that is fixed in relation to the LIDAR sensor assembly. A controller of the LIDAR sensor assembly can process a plurality of samples of reflected light signals, process the samples to remove erroneous readings, and then provide accurate distance measurement. The system can use low-pass filters, or other components, to filter the plurality of samples to enable the “actual,” or primary, reflected light signal (i.e., light signal reflected off of a surface in an environment external to the sensor assembly, as opposed to extraneous, internal reflections off of lenses or other components or noise) to be identified and an accurate time of flight to be calculated.