Vehicle Audio Localization for Emergency Siren Direction Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting and responding to emergency vehicles in complex environments, where auditory and visual cues are used to indicate priority, and distinguishing between relevant and irrelevant sounds is difficult.
Innovation Solution
A machine-learned model is employed to classify audio data from multiple sensors, determining the likelihood of emergency vehicle sounds and their direction of arrival, enabling the vehicle to quickly and accurately navigate and respond to priority cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple audio sensors are used to detect emergency vehicles, then the detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent divides the audio detection task into multiple independent audio sensors positioned at different locations on the vehicle. Each sensor captures audio data from its specific direction, and the system processes these segmented inputs separately before integrating them to determine the direction of arrival, thereby improving detection accuracy while managing complexity through modular processing
Solution Approach 2:
The patent combines data from multiple audio sensors to determine the direction of arrival of emergency vehicle sounds. By merging the audio data and classification results from multiple sensors, the system achieves more accurate localization of emergency vehicles than a single sensor could provide, resolving the contradiction between using multiple sensors and system complexity
2Measurement precision
If a machine-learned model is used to classify audio data, then the classification accuracy is improved, but the computational processing time increases
Solution Approach 1:
The patent applies a machine-learned model to classify audio data from multiple sensors simultaneously and in advance of the direction determination step. By performing the classification action preliminarily, the system prepares processed results that can be quickly used for direction of arrival calculation, reducing the computational burden during critical real-time response moments
Solution Approach 2:
The patent replaces traditional audio processing methods with a machine-learned model that automatically classifies audio data. This substitution enables more accurate classification of emergency vehicle sounds from background noise while the model's optimized architecture processes the data efficiently, balancing accuracy with computational speed
3Reliability
If the vehicle responds quickly to emergency vehicles, then the safety is improved, but the detection reliability may be compromised due to reduced processing time
Solution Approach 1:
The system performs preliminary classification of audio data using the machine-learned model before the vehicle needs to respond. This advance processing ensures that when an emergency vehicle is detected, the classification results are already available, enabling quick response without compromising detection reliability since the thorough classification has already been completed
Solution Approach 2:
The machine-learned model acts as an intermediary between raw audio data and the vehicle's response system. It processes and classifies audio data to identify emergency vehicles, providing reliable detection results that enable safe and timely vehicle responses while filtering out false alarms from non-emergency sounds
Data Source
AI summary
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.