Effective Rolling Radius Calibration via GPS and Wheel Data
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Solution Overview
Problem
Existing vehicle control systems lack accuracy in estimating the effective rolling radius of wheels, which is crucial for precise vehicle control, due to variations in weight, temperature, pressure, wear, and road conditions, especially in autonomous or semi-autonomous vehicles.
Innovation Solution
A system that utilizes image data from sensors like lidar, radar, and cameras to determine global positioning data, estimate effective rolling radius, and calculate vehicle velocity, incorporating wheel revolution data to refine positional resolution and control vehicle functions such as steering, throttle, and braking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to estimate effective rolling radius, then the system complexity is low, but the measurement precision is insufficient due to variations in weight, temperature, pressure, wear, and road conditions
Solution Approach 1:
The patent uses GPS positioning data as an intermediary to indirectly measure the effective rolling radius. Instead of directly measuring wheel parameters under varying conditions, the system uses the relationship between GPS-derived distance and wheel revolution count to calculate the effective rolling radius, thereby avoiding direct measurement complexity while improving accuracy
Solution Approach 2:
The system continuously updates the effective rolling radius estimation by comparing GPS-measured distance with wheel revolution data, creating a feedback mechanism that adapts to changing conditions (weight, temperature, pressure, wear, road conditions) and maintains measurement precision without requiring complex direct sensing of each parameter
2Measurement precision
If GPS data alone is used for positioning, then the device complexity is low, but the measurement precision is insufficient for accurate wheel radius estimation
Solution Approach 1:
The patent merges GPS positioning data with wheel revolution counter data to achieve high-precision positional resolution. By combining these two data sources, the system achieves accuracy sufficient for effective rolling radius estimation without requiring complex additional positioning hardware
Solution Approach 2:
The system uses multi-functional data processing where GPS data serves both for vehicle positioning and for calculating travel distance, which is then combined with wheel revolution data to determine effective rolling radius. This multi-functionality achieves high precision without adding dedicated measurement devices
Data Source
AI summary
Systems and method are provided for controlling a vehicle. In one embodiment, a method includes receiving, via a processor, image data of a surroundings of the vehicle. The method includes performing, via a processor, image analysis on the image data to identify road features. The method includes matching, via a processor, the identified road features to road features in a predetermined map to determine matched road features. The method includes determining, via a processor, global positioning data for the vehicle based on global positioning data in the predetermined map and the matched road features. The method also includes calibrating, via a processor, effective rolling radius of a wheel of the vehicle based at least on the global position data. The method further includes controlling, via a processor, a function of the vehicle based, in part, on the effective rolling radius.