Contextual Acoustic Scene Analysis with HAPM for Blind Spots
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Solution Overview
Problem
Existing vehicular automation systems (VASs) rely heavily on line-of-sight sensors, which are limited by blind spots and obscured hazards, leading to insufficient situational awareness and safety risks, while acoustic sensors are underutilized due to lack of reliability and precision in processing acoustic signals for safety-critical applications.
Innovation Solution
Integrate acoustic sensors with VASs, using a neural network and Human Auditory Perception Model (HAPM) to process acoustic data, incorporating contextual metadata to mimic human perception, and adjust processing scores based on environmental conditions, enhancing situational awareness and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If line-of-sight sensors (cameras, LiDAR, radar) are used to detect hazards, then detection accuracy is improved within direct visual path, but blind spots and obscured hazards cannot be detected
Solution Approach 1:
The patent combines line-of-sight sensors (cameras, LiDAR, radar) with acoustic sensors (microphones) into an integrated sensor suite. Acoustic sensors detect sound waves from hazards around corners or behind obstacles, complementing the visual data from line-of-sight sensors and filling blind spots.
Solution Approach 2:
Acoustic sensors serve as an intermediary detection mechanism that can perceive hazards indirectly through sound propagation, which travels around obstacles differently than light. This intermediary sensing modality bridges the information gap created by line-of-sight limitations.
2Reliability
If acoustic sensors are added to improve detection of obscured hazards, then situational awareness is improved, but system complexity increases
Solution Approach 1:
The acoustic sensor system is designed to perform multiple functions: detecting obscured hazards, classifying acoustic events (vehicle types, animal sounds), and providing data to both immediate processing systems and remote servers. This multi-functionality justifies the added complexity by delivering diverse value from a single sensor addition.
Solution Approach 2:
The acoustic processing system is segmented into multiple components: edge-based processing units in the vehicle, remote server-based machine learning models, and hierarchical processing levels. This segmentation distributes computational complexity across different locations and time scales, making the overall system more manageable.
3Measurement precision
If machine learning algorithms are used to process acoustic data, then hazard detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Acoustic events are pre-classified at the edge device into categories (vehicle types, animal sounds, human sounds) using lightweight machine learning models. This preliminary classification prepares data in advance for more sophisticated analysis, reducing real-time processing requirements while maintaining high accuracy.
Solution Approach 2:
The system dynamically adjusts processing depth based on context: simple acoustic events are processed quickly at the edge, while complex or ambiguous events trigger more computationally intensive remote server analysis. This dynamic approach optimizes the balance between processing time and accuracy.
Data Source
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AI summary
The present disclosure is directed towards systems and methods of processing audio information for a vehicular automation system. The method comprises obtaining a context of a VAS; obtaining one or more audio data streams from one or more audio sensors; extracting an audio feature from the one or more audio data streams; processing the audio feature with a neural network and a vector space model to determine the relevance of the audio feature, wherein: the vector space model comprises a Human Audio Perception Model, HAPM; the HAPM is configured to mimic how an audio signal is perceived and processed by a human; and the method comprises using the HAPM to adjust the relevance of the feature based on the context of the VAS.