Abstract: Techniques for utilizing a depth completion algorithm to determine dense depth data are discussed are discussed herein. Two-dimensional image data representing an environment can be captured or otherwise received. Depth data representing the environment can be captured or otherwise received. The depth data can be projected into the image data and processed using the depth completion algorithm. The depth completion algorithm can be utilized to determine the dense depth values based on intensity values of pixels, and other depth values. A vehicle can be controlled based on the determined depth values.

Abstract: Techniques for utilizing microphone or audio data to detect and responding to low velocity impacts to a system such as an autonomous vehicle. In some cases, the system may be equipped with a plurality of microphones that may be used to detect impacts that fail to register on the data captured by the vehicle's inertial measurement units and may go undetected by the vehicle's perception system and sensors. In one specific example, the perception system of the autonomous vehicle may identify a period of time in which a potential low velocity impact may occur. The autonomous vehicle may then utilize the microphone or audio data associated with the period of time to determine if an impact occurred.

Abstract: Techniques for utilizing a depth completion algorithm to determine dense depth data are discussed are discussed herein. Two-dimensional image data representing an environment can be captured or otherwise received. Depth data representing the environment can be captured or otherwise received. The depth data can be projected into the image data and processed using the depth completion algorithm. The depth completion algorithm can be utilized to determine the dense depth values based on intensity values of pixels, and other depth values. A vehicle can be controlled based on the determined depth values.

Abstract: Techniques for determining information associated with sounds detected in an environment based on audio data are discussed herein. Audio sensors of a vehicle may determine audio data associated with sounds from the environment. Sounds may be caused by objects in the environment such as emergency vehicles, construction zones, non-emergency vehicles, humans, audio speakers, nature, etc. A model may determine a classification of the audio data and/or a probability value representing a likelihood that sound in the audio data is associated with the classification. A direction of arrival may be determined based on receiving classification values from multiple audio sensors of the vehicle, and other actions can be performed or the vehicle can be controlled based on the direction of arrival.

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 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 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.