Vehicle Audio Classification via Neural Network for Occupant Safety
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Solution Overview
Problem
Existing automotive safety systems lack the intelligence to recognize and respond to young occupants left alone in vehicles, where rising temperatures can pose a significant risk, especially in hot conditions, and fail to proactively alert owners or bystanders to potential dangers such as theft or battery explosions.
Innovation Solution
An audio recognition system utilizing neural network machine learning to classify sounds and determine their origin, integrating with vehicle sensors to assess conditions and trigger alerts or warning signals, such as flashing headlights and honking horns, to address risks to occupants and the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional safety systems are used in vehicles, then basic safety functions are provided, but the system lacks intelligence to recognize and respond to young occupants left alone in vehicles
Solution Approach 1:
The patent replaces traditional mechanical safety systems with an audio-based detection system using microphones and neural network machine learning algorithms. The system captures audio signals from the vehicle interior, processes them through neural networks to classify sounds and identify young occupants, and triggers appropriate safety responses. This substitution enables intelligent recognition capabilities that mechanical systems cannot provide.
Solution Approach 2:
The patent introduces audio signals as an intermediary medium between the young occupant and the safety system. Instead of direct contact or visual detection, the system uses audio cues (crying, breathing sounds) as intermediaries to detect the presence and condition of young occupants, enabling indirect but effective monitoring.
2Adaptability or versatility
If the vehicle is equipped with advanced audio recognition and neural network processing, then intelligent detection of young occupants is achieved, but device complexity increases
Solution Approach 1:
The patent makes the audio recognition system universal by designing it to perform multiple functions: detecting young occupants, classifying different sound types (crying, breathing, ambient noises), determining sound origins (inside vs. outside vehicle), and triggering various safety responses. This multi-functionality reduces the need for separate specialized systems for each task.
Solution Approach 2:
The neural network machine learning system performs self-service by automatically learning from training data and improving its sound classification capabilities without manual intervention. The system self-adjusts its parameters and algorithms to optimize detection accuracy, reducing the need for complex manual configuration and maintenance.
3Measurement precision
If the system continuously monitors audio signals and processes them through neural networks, then detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic monitoring where the audio recognition system activates at specific intervals or under specific conditions (e.g., when the vehicle is parked, when motion sensors detect no adult movement) rather than continuous operation. This periodic action maintains detection accuracy for young occupants while significantly reducing overall energy consumption compared to constant monitoring.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on relevant audio segments that contain potential indicators of young occupant presence. Instead of processing all audio data equally, the neural network selectively analyzes portions of the audio signal that are most likely to contain meaningful information, reducing total processing energy while maintaining detection accuracy.
Data Source
AI summary
A method and an apparatus for detecting and classifying sounds around a vehicle via neural network machine learning are described. The method involves an audio recognition system that may determine the origin of the sounds being inside or outside of a vehicle and classify the sounds into different categories such as adult, child, or animal sounds. The audio recognition system may communicate with a plurality of sensors in and around the vehicle to obtain information of conditions of the vehicle. Based on information of the sounds and conditions of the vehicles, the audio recognition system may determine whether an occupant or the vehicle is at risk and send alert messages or issue warning signals.