Collision avoidance using auditory data
First Claim
1. A system for an autonomous vehicle comprising:
- two or more microphones mounted to the autonomous vehicle;
a controller executinga pre-processor programmed to detect audio features in two or more audio streams from the two or more microphones;
a collision avoidance module programmed to classify the audio features and a direction to a source thereof, and, if the class for the sound source is a vehicle, invoke obstacle avoidance with respect to the direction;
wherein the collision avoidance module is further programmed to;
classify the audio features by inputting the audio features into a machine learning model; and
wherein the machine-learning model outputs a confidence value indicating a probability that the audio features correspond to a vehicle.
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Abstract
A controller for an autonomous vehicle receives audio signals from one or more microphones. The outputs of the microphones are pre-processed to enhance audio features that originated from vehicles. The outputs may also be processed to remove noise. The audio features are input to a machine learning model that classifies the source of the audio features. For example, features may be classified as originating from a vehicle. A direction to a source of the audio features is determined based on relative delays of the audio features in signals from multiple microphones. Where audio features are classified with an above-threshold confidence as originating from a vehicle, collision avoidance is performed with respect to the direction to the source of the audio features.
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Citations
16 Claims
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1. A system for an autonomous vehicle comprising:
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two or more microphones mounted to the autonomous vehicle; a controller executing a pre-processor programmed to detect audio features in two or more audio streams from the two or more microphones; a collision avoidance module programmed to classify the audio features and a direction to a source thereof, and, if the class for the sound source is a vehicle, invoke obstacle avoidance with respect to the direction; wherein the collision avoidance module is further programmed to; classify the audio features by inputting the audio features into a machine learning model; and wherein the machine-learning model outputs a confidence value indicating a probability that the audio features correspond to a vehicle. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for obstacle detection in an autonomous vehicle, the method comprising:
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receiving, by a controller including one or more processing devices, two or more audio streams from two or more microphones mounted to the autonomous vehicle; detecting, by the controller, audio features in the two or more audio streams; detecting, by the controller, a direction to a sound source according to the audio features; identifying, by the controller, a class for the sound source according to the audio features; and determining, by the controller, that the class for the sound source is a vehicle, in response to determining that the class for the sound source is a vehicle, invoking obstacle avoidance with respect to the direction to the sound source; wherein identifying the class for the sound source according to the audio features comprises inputting, by the controller, the audio features into a machine learning model; and wherein the method further comprises outputting, by the machine-learning model, a confidence value indicating a probability that the audio features correspond to a vehicle. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification