Active Road Noise Cancellation Tuning with Gradient Optimization
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Solution Overview
Problem
Conventional active road noise cancellation systems require manual tuning of parameters, which is time-consuming and resource-intensive, and do not adapt effectively to varying driving conditions.
Innovation Solution
A method and system for automatically tuning road noise cancellation parameters using a software simulation and gradient-based optimization techniques, leveraging recorded data from test vehicles to determine optimal parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning of parameters is used for active road noise cancellation systems, then the system can be properly configured for optimal performance, but the process requires significant time, resources, and expertise
Solution Approach 1:
The patent creates a virtual copy of the noise cancellation system through software simulation. This digital twin allows parameter tuning to be performed in the virtual environment using recorded logs from test vehicles, eliminating the need for time-consuming manual tuning of physical systems while maintaining tuning precision through iterative optimization algorithms.
Solution Approach 2:
The system performs preliminary parameter tuning through automated simulation before actual deployment. By using recorded driving condition logs and running virtual experiments beforehand, the optimal parameters are determined in advance, saving significant time during actual system implementation and deployment.
2Reliability
If manual parameter tuning is performed, then optimal performance can be achieved, but expert knowledge and resources are required
Solution Approach 1:
The noise cancellation system performs self-tuning through automated algorithms that independently optimize parameters using simulation software and recorded data. The system serves itself by automatically determining optimal parameters without requiring external expert intervention, thereby maintaining high reliability while reducing process complexity and resource requirements.
Solution Approach 2:
The patent replaces the manual mechanical tuning process with an automated computational system. Instead of experts manually adjusting parameters, a software-based optimization system using gradient descent and simulation automatically determines optimal settings, reducing human expertise requirements while maintaining or improving performance reliability.
3Object-affected harmful factors
If conventional noise cancellation systems are used, then they can suppress noise in vehicle cabins, but they do not adapt effectively to varying driving conditions
Solution Approach 1:
The system implements dynamic parameter adaptation by using recorded logs from various driving conditions to train the simulation model. The parameters are optimized to adapt to different scenarios such as various road surfaces, speeds, and vehicle loads, enabling the noise cancellation system to dynamically adjust to varying driving conditions while maintaining effective noise suppression.
Solution Approach 2:
The patent employs parameter changes based on driving conditions by using the software simulation to determine optimal parameter sets for different scenarios. The system adjusts parameters such as filter coefficients and adaptive algorithm settings based on the specific driving conditions represented in the recorded logs, thereby improving adaptability while maintaining noise suppression performance.
Data Source
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AI summary
Techniques for automatic parameter tuning of active road noise cancellation systems are described herein. The system can automatically search for an optimal set of algorithm parameters based on recorded data. An active road noise cancellation algorithm and simulation can be embedded in an auto-differentiation framework, which allows gradients of the algorithm parameters to guide the automatic search and calculations of the algorithm parameters.