Vehicle Yaw Rate Model Calibration via Cumulative Error Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle steering control systems face challenges in accurately predicting and controlling yaw rates, especially during changes in tire conditions, vehicle modifications, or environmental changes, leading to inaccuracies in steering control.
Innovation Solution
A method and apparatus for vehicle control that involves receiving data related to steering, detecting target data that satisfies predetermined conditions, and using an optimization model to obtain optimized model parameters that minimize the cumulative error between predicted and measured yaw rates, thereby updating the yaw rate model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a preset steering control model is used for vehicle steering control, then the system structure is simple and easy to implement, but the accuracy of predicted yaw rates deteriorates when tire conditions change, vehicle modifications occur, or environmental conditions change
Solution Approach 1:
The patent implements a dynamic calibration optimization system that automatically updates yaw rate model parameters based on real-time vehicle operating conditions. The system transitions from a static preset model to a dynamic adaptive model that continuously learns and adjusts to tire replacements, vehicle modifications, and environmental changes, thereby maintaining high prediction accuracy without increasing fundamental system complexity
Solution Approach 2:
The patent employs a feedback mechanism where the actual yaw rate measurements are continuously compared with predicted yaw rates from the preset model. The cumulative error feedback triggers automatic calibration optimization when thresholds are exceeded, allowing the system to self-correct and maintain accuracy without manual intervention, thus resolving the contradiction between simple structure and high precision
2Measurement precision
If calibration is performed manually on the steering control model, then the accuracy of predicted yaw rates improves, but the time and labor required for calibration increases
Solution Approach 1:
The patent implements a self-service calibration system where the vehicle's own operating data and yaw rate measurements are used to automatically perform calibration optimization. The system monitors its own performance, detects when calibration is needed through error threshold evaluation, and executes the calibration process autonomously without requiring external technicians or manual intervention, thereby achieving high accuracy while eliminating time and labor losses
Solution Approach 2:
The patent performs calibration optimization in advance automatically when error thresholds are detected, rather than waiting for manual calibration requests. The system proactively updates the yaw rate model parameters before significant performance degradation occurs, ensuring continuous high accuracy without requiring scheduled manual calibration time
3Measurement precision
If the yaw rate model parameters are updated frequently to maintain accuracy, then the precision of steering control improves, but the computational load and system complexity increase
Solution Approach 1:
The patent implements periodic calibration optimization triggered by cumulative error thresholds rather than continuous updates. The system accumulates yaw rate prediction errors and only initiates calibration optimization when the cumulative error exceeds a predetermined threshold, creating a periodic update pattern that maintains precision while minimizing unnecessary computational load and system complexity
Solution Approach 2:
The patent changes the calibration trigger parameter from continuous monitoring to cumulative error threshold evaluation. By accumulating errors over time and comparing against a threshold, the system achieves effective calibration at optimal intervals, balancing precision requirements with computational efficiency and avoiding excessive parameter updates
Data Source
AI summary
A method of controlling a vehicle includes: receiving pieces of data related to steering of the vehicle; detecting, among the pieces of data, target pieces of data determined to satisfy predetermined conditions; based on the target pieces of data and an optimization model, obtaining optimized model parameters that minimize a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle; and updating the yaw rate model of the vehicle by using the optimized model parameters.