Tire-Road Friction Estimation Using GPS and Tire Force Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately estimating tire-road friction due to varying road conditions, which can lead to unpredictable vehicle control and safety issues.
Innovation Solution
A method and system that estimate the coefficient of friction between a tire and the road by combining dynamic force measurements with a tire model, using sensors and processors to determine friction coefficients based on slip angles and yaw parameters, and adjust vehicle control accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road friction is estimated using conventional methods, then the estimation may be insufficient under varying road conditions, but the vehicle control reliability deteriorates when road conditions change rapidly
Solution Approach 1:
The system continuously updates the friction coefficient estimate by comparing the measured tire force (from vehicle dynamics) with the model-based tire force, using this feedback to adjust the friction estimate in real-time as road conditions change
Solution Approach 2:
The system proactively detects low friction conditions by monitoring the discrepancy between measured and model-based forces, and preemptively adjusts vehicle control parameters before slipping or loss of control occurs
2Measurement precision
If a simple friction estimation method is used, then the device complexity is low, but the measurement precision of friction estimation is insufficient
Solution Approach 1:
The system uses the vehicle's own existing operational data (forces, accelerations, steering angles) and a tire model to self-determine the friction coefficient, eliminating the need for external sensors or complex additional hardware
Data Source
AI summary
A vehicle and a system and method of controlling the vehicle. The system includes a sensor and a processor. The sensor obtains a first estimate of a force on a tire of the vehicle based on dynamics of the vehicle. The processor is configured to obtain a second estimate of the force on the tire using a tire model, determine an estimate of a coefficient of friction between the tire and the road from the first estimate of the force and the second estimate of the force, and control the vehicle using the estimate of the coefficient of friction.