Vehicle Class Identification Using Audio Data and Machine Learning
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Solution Overview
Problem
Current vehicle sensors do not collect all types of information necessary for various applications, limiting their effectiveness in determining vehicle class and providing relevant data for navigation, regulation, and maintenance.
Innovation Solution
A method and apparatus that utilize audio data collected by a vehicle while driving over a road surface, including audio amplitude and frequency features, to predict the vehicle class through a machine learning model, supplemented by map object information and vehicle location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional vehicle sensors are used for data collection, then the device complexity is low, but the quantity and variety of useful information collected is insufficient
Solution Approach 1:
The existing vehicle sensors are made multi-functional by applying machine learning algorithms to extract multiple types of information (vehicle class, road surface conditions, location) from the same audio data, allowing one sensor system to serve multiple purposes without adding more sensors
Solution Approach 2:
The patent replaces physical sensor additions with computational processing - using machine learning models to analyze audio data and infer vehicle class and road conditions, substituting what would otherwise require additional mechanical sensors
2Measurement precision
If more sensors are added to collect comprehensive vehicle information, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent substitutes additional mechanical sensors with computational analysis of existing audio sensor data, using machine learning to achieve accurate vehicle class identification without adding physical sensing components
Solution Approach 2:
The patent changes the parameter being measured from raw audio signals to derived features such as frequency spectrum characteristics and amplitude patterns, which when fed into machine learning models enable accurate vehicle class determination
3Loss of information
If audio data is used to determine vehicle class, then the loss of information is reduced, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between raw audio data and vehicle class identification, automatically extracting relevant features and patterns that would be difficult to detect through traditional methods
Data Source
AI summary
A method, apparatus and computer program product are provided to identify the class of vehicle driving over a road surface based upon audio data collected as the vehicle drives thereover. With respect to predicting a class of a vehicle, audio data is obtained that is created by the vehicle while driving over the road surface. The audio data includes one or more audio frequency features and/or one or more audio amplitude features. The audio data including the one or more audio frequency features and/or the one or more audio amplitude features is provided to a machine learning model and the class of the vehicle that created the audio data is predicted utilizing the machine learning model. A method, apparatus and computer program product are also provided for training the machine learning model to predict the class of the vehicle driving over the road surface.