Autonomous Vehicle Audio Signal Detection Using Microphone Arrays
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Solution Overview
Problem
Current semi-autonomous and autonomous driving systems lack effective methods to detect and respond to emergency vehicles or urgent situations using audio signals in their environment, which is crucial for safe navigation and interaction.
Innovation Solution
The implementation of a computer-aided driving system that utilizes a network of microphones to capture audio signals, an audio analysis module to divide and extract features, and a Deep Neural Network classifier to identify alarm signals, enabling the system to autonomously respond to emergency vehicles by controlling vehicle navigation and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio analysis is added to detect emergency vehicles, then safety and responsiveness are improved, but device complexity increases
Solution Approach 1:
The existing autonomous driving system is extended to perform multiple functions by integrating audio signal processing capabilities into the current sensor fusion architecture, allowing the system to simultaneously handle visual, auditory, and other environmental data without creating entirely separate systems
Solution Approach 2:
An audio analysis module is introduced as an intermediary component that processes raw audio signals from microphones, extracts relevant features, and feeds processed information to the decision-making system, thereby managing complexity through modular intermediate processing stages
2Measurement precision
If Deep Neural Network classifier is implemented to identify alarm signals, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The audio signal processing is divided into distinct segments: raw audio capture, feature extraction, neural network classification, and decision-making. This segmentation allows the system to process only relevant audio data through the energy-intensive DNN classifier rather than continuous full-spectrum analysis
Solution Approach 2:
The system applies the DNN classifier selectively to audio frames that contain potential alarm signal characteristics identified through preliminary feature extraction, rather than applying full classification power to all audio data continuously, thereby reducing overall energy consumption while maintaining detection accuracy
3Reliability
If audio signal processing is continuously performed, then detection reliability is improved, but loss of energy increases
Solution Approach 1:
The audio analysis is performed periodically at strategically chosen intervals rather than continuously, with the processing frequency adjusted based on detected audio event characteristics and vehicle operational context, maintaining reliable detection while minimizing unnecessary energy consumption during low-risk periods
Data Source
AI summary
Embodiments include apparatuses, systems, and methods for a computer-aided or autonomous driving (CA/AD) system to identify and respond to an audio signal, e.g., an emergency alarm signal. In embodiments, the CA/AD driving system may include a plurality of microphones disposed to capture the audio signal included in surrounding sounds to a semi-autonomous or autonomous (SA/AD) vehicle. In embodiments, an audio analysis unit may receive the audio signal to extract audio features from the audio signal. In embodiments, a neural network such as a Deep Neural Network (DNN) may receive the extracted audio features from the audio analysis unit and to generate a probability score to allow identification of the audio signal. In embodiments, the CA/AD driving system may control driving elements of the SA/AD vehicle to autonomously or semi-autonomously drive the SA/AD vehicle in response to the identification. Other embodiments may also be described and claimed.