Active Road Noise Cancellation Parameter Tuning Using Recorded Logs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional active road noise cancellation systems require manual tuning of parameters, which is time-consuming, resource-intensive, and requires expertise, making them inefficient for optimal performance.
Innovation Solution
A method and system for automatically setting tunable parameter values in road noise cancellation systems using a software simulation and gradient-based optimization techniques, utilizing recorded logs from different driving conditions to determine optimal parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of parameters is performed for optimal noise cancellation performance, then noise cancellation effectiveness is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs automatic parameter tuning using recorded logs and software simulations, enabling the noise cancellation system to self-optimize without requiring manual human intervention. The tuning process is automated through algorithms that analyze recorded driving conditions and simulate parameter adjustments to determine optimal settings.
Solution Approach 2:
The system conducts parameter tuning in advance using recorded logs from test vehicles and software simulations before actual deployment. This preliminary optimization allows the system to pre-determine optimal parameters based on simulated driving conditions, reducing the need for time-consuming manual tuning during installation or operation.
2Reliability
If manual parameter tuning is performed by experts, then optimal performance is achieved, but expertise requirements and resource costs increase
Solution Approach 1:
The patent replaces the manual expert tuning process with an automated computational system. Instead of relying on human experts to manually adjust parameters, the system uses software simulations and algorithms to automatically determine optimal parameters, substituting mechanical human intervention with automated digital processing.
Solution Approach 2:
The system creates a virtual copy of the noise cancellation system through software simulation. This digital twin allows parameters to be tuned and optimized in the virtual environment using recorded logs, and the optimized parameters are then applied to the actual physical system, eliminating the need for complex manual tuning procedures.
3Object-affected harmful factors
If conventional active road noise cancellation systems are implemented, then noise suppression is achieved, but adaptability to varying driving conditions is limited
Solution Approach 1:
The system dynamically adapts to varying driving conditions by using recorded logs from different scenarios and performing software simulations for each condition. Instead of using fixed parameters, the system adjusts parameters based on the specific driving conditions simulated, enabling dynamic optimization for different road surfaces, vehicle speeds, and operating environments.
Solution Approach 2:
The system changes parameters based on simulated driving conditions. By analyzing recorded logs representing different driving scenarios and running software simulations, the system determines optimal parameter settings for each condition, allowing the noise cancellation system to adapt its parameters dynamically rather than using fixed conventional settings.
Data Source
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.