Friction Adaptive Vehicle Control via Probabilistic Tire Stiffness Estimation
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Solution Overview
Problem
Current vehicle control systems face challenges in accurately measuring and adapting to tire friction in real-time, especially during aggressive driving, due to the non-linear nature of the tire-force relationship and the limitations of automotive-grade sensors, which are prone to noise and biases, making it difficult to estimate tire friction without expensive and unreliable force sensors.
Innovation Solution
A system and method that estimate tire friction using a friction function parameterized by linear and non-linear parameters, allowing control of vehicle motion without direct measurement of tire friction, by selecting appropriate friction parameters based on vehicle state and road conditions, using a probabilistic filter to determine the current state of tire stiffness and selecting the most likely friction function for control commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If force sensors are used to directly measure tire friction, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses an intermediary estimation approach where tire friction is not measured directly by force sensors, but inferred through a probabilistic filter that processes measurements from existing automotive-grade sensors (wheel speed sensors, steering angle sensors, brake pressure sensors). The filter estimates tire stiffness and selects friction parameters based on vehicle state and road conditions, serving as an intermediary between available sensor data and friction information.
Solution Approach 2:
The patent replaces the mechanical force sensing system with a computational estimation system. Instead of using physical force sensors to directly measure tire-road interaction forces, the system substitutes a probabilistic algorithm that processes electrical signals from standard sensors and computes friction characteristics through mathematical modeling and parameter selection.
2Device complexity
If automotive-grade sensors are used to estimate tire friction, then device complexity is reduced, but measurement precision deteriorates due to noise and biases
Solution Approach 1:
The patent implements a feedback mechanism where the probabilistic filter continuously processes sensor measurements and updates its estimates of tire stiffness and friction parameters. The system uses feedback from wheel speed, steering angle, and brake pressure measurements to adjust friction parameter selection in real-time, compensating for noise and biases in the automotive-grade sensors through iterative estimation and parameter reselection.
Solution Approach 2:
The patent changes parameters dynamically by selecting different friction function parameters based on the current vehicle state and estimated tire stiffness. The system adjusts friction parameters (such as friction coefficient and slip characteristics) according to operating conditions, allowing accurate friction estimation despite the limitations of fixed-characteristic automotive sensors.
3Adaptability or versatility
If tire friction is estimated in real-time during vehicle operation, then adaptability is improved, but difficulty of detecting and measuring increases due to non-linear tire-force relationship
Solution Approach 1:
The patent applies dynamics by making the friction parameter selection dynamic rather than static. The probabilistic filter continuously adapts friction parameters based on real-time vehicle state changes, tire stiffness estimates, and road condition variations. This dynamic approach allows the system to handle the non-linear tire-force relationship by adjusting parameters on-the-fly rather than relying on fixed friction models.
Solution Approach 2:
The patent segments the friction estimation problem into manageable parts: the probabilistic filter separately estimates tire stiffness from sensor measurements, selects appropriate friction functions based on stiffness ranges, and then determines friction parameters for control use. This segmentation breaks down the complex non-linear estimation task into sequential, more tractable sub-tasks.
Data Source
AI summary
A system control a vehicle using a friction function describing a friction between a type of surface of the road and a tire of the vehicle as a function of a slip of a wheel of the vehicle. The parameters of each friction function include an initial slope of the friction function defining a stiffness of the tire and one or combination of a peak friction, a shape factor and a curvature factor of the friction function. Upon estimating a slip and a stiffness of the tire, the system selects from the memory parameters of the friction function corresponding to the current stiffness of the tire, determines a control command using a value of the friction corresponding to the slip of the tire according to the friction function defined by the selected parameters, and submits the control command to an actuator of the vehicle.