Vehicle Cabin Audio Control Using Neural Network Predictions
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Solution Overview
Problem
Current autonomous driving systems lack the ability to predict component failures in vehicles, leading to unexpected breakdowns and inconvenient maintenance schedules, which can compromise safety and convenience.
Innovation Solution
Implementing a data storage device in vehicles equipped with sensors and an artificial neural network (ANN) that collects and analyzes sensor data to predict maintenance needs, allowing for proactive scheduling of maintenance services and reducing the likelihood of breakdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving systems operate without predictive maintenance capabilities, then the system complexity remains low, but vehicle reliability deteriorates due to unexpected breakdowns
Solution Approach 1:
The system performs preliminary analysis of sensor data to predict component failures before they occur. The neural network continuously monitors vehicle sensor data and identifies patterns that precede component failures, enabling maintenance to be scheduled proactively rather than reactively, thus improving reliability without requiring complex real-time intervention systems
Solution Approach 2:
The predictive maintenance system serves itself by using the same sensor infrastructure already present in autonomous vehicles. The existing sensors capture data that is repurposed for maintenance prediction, eliminating the need for separate dedicated monitoring hardware and reducing overall system complexity while maintaining high reliability
2Ease of operation
If predictive maintenance analysis is performed continuously, then maintenance scheduling convenience improves, but energy consumption increases
Solution Approach 1:
The system performs predictive analysis periodically rather than continuously. The neural network processes sensor data at scheduled intervals to generate maintenance predictions, which provides sufficient information for convenient maintenance scheduling while avoiding the excessive energy consumption of continuous real-time analysis
Solution Approach 2:
The system uses an intermediary processing layer that aggregates and pre-processes sensor data before feeding it to the neural network. This intermediary step reduces the computational burden and energy consumption by filtering and consolidating data, while still providing accurate maintenance predictions for convenient scheduling
3Reliability
If component failure prediction is implemented, then breakdown risk reduces, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The system makes existing multi-functional sensor data serve dual purposes: both autonomous driving operations and predictive maintenance analysis. The same sensors that capture environmental data for navigation also monitor vehicle component conditions, eliminating the need for separate dedicated monitoring sensors and reducing overall system complexity
Solution Approach 2:
The neural network processing is merged with the existing autonomous driving decision-making architecture. The same computational resources and processing pipelines used for navigation and control are leveraged to perform maintenance predictions, combining multiple functions into a unified system rather than adding separate independent subsystems
Data Source
AI summary
Systems, methods and apparatus of control delivery of audio content into a vehicle cabin where occupants of vehicles are located/seated. For example, a vehicle includes: at least one microphone configured to generate signals representing audio content presented in the cabin of the vehicle; an infotainment system having access to multiple sources of audio content; and an artificial neural network configured to receive input parameters relevant to audio control in the vehicle and generate, based on the input parameters as a function of time, predictions of audio pattern. The input parameters can include data representing the signals from the at least one microphone, data from the infotainment system, and/or at least one operating parameter of the vehicle. The vehicle is configured to adjust a setting of the infotainment system based at least in part on the predictions generated by the artificial neural network.