Vehicle Friction Approximation Using Steering Control Deviations
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Solution Overview
Problem
Existing methods for assessing friction between vehicle wheels and the roadway are inaccurate, particularly under varying conditions, posing a safety risk for both human and autonomous driving.
Innovation Solution
A method that determines load characteristics, setpoint and actual variables, and manipulated variable deviations to approximate friction values using existing vehicle sensors, incorporating environmental indicators and historical data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical sensors are used to assess roadway conditions, then visual information about the roadway can be captured, but the results are strongly influenced by sensor properties and cannot be used in all driving situations (e.g., poor light conditions)
Solution Approach 1:
The patent introduces an intermediary physical model that translates between observable vehicle behavior (manipulated variables) and friction conditions. This model acts as a mediator that is independent of optical sensor limitations, enabling friction assessment through vehicle dynamics data instead of direct roadway visualization.
Solution Approach 2:
The patent replaces optical/mechanical sensing systems with a model-based approach using vehicle control data. Instead of using cameras or optical sensors to directly observe roadway conditions, the system substitutes this with a mathematical model that infers friction from manipulated variables and their deviations, eliminating dependency on optical sensor properties.
2Measurement precision
If optical systems are used to assess roadway adhesion, then roadway aspects can be considered, but vehicle-specific aspects are neglected
Solution Approach 1:
The patent creates a universal assessment model that simultaneously considers both roadway conditions and vehicle-specific aspects through a single friction value approximation. The model integrates manipulated variables from vehicle control systems with physical models that account for both external roadway properties and internal vehicle characteristics, making the system multi-functional in its assessment capabilities.
3Reliability
If human drivers assess friction values visually and acoustically, then experience-based judgment can be made, but unpracticed drivers can incorrectly assess the friction value, posing significant safety risk
Solution Approach 1:
The patent enables the vehicle's control system to automatically assess friction values using its own operational data (manipulated variables, setpoint variables, actual variables). The system serves itself by utilizing existing vehicle sensors and control data without requiring external assessment tools or human intervention, thereby providing reliable friction information regardless of driver experience.
4Reliability
If a reliable friction value assessment method is implemented, then safe vehicle control can be achieved, but existing optical sensor-based approaches have multiple disadvantages and are not sufficiently accurate
Solution Approach 1:
The patent implements a feedback mechanism where the approximation model continuously refines friction value estimation by comparing manipulated variable deviations with expected behavior. The system uses feedback from vehicle response to control inputs to iteratively improve the accuracy of friction assessment, ensuring reliable and precise measurements for safe vehicle control.
Data Source
AI summary
A method for approximating a friction value includes: determining a load characteristic; determining a setpoint variable of the vehicle; determining a manipulated variable expected value specifying a predicted value of a manipulated variable to be provided to set the setpoint variable, wherein the determination of the manipulated variable expected value is performed using the load characteristic; determining an actual variable corresponding to the setpoint variable; determining a manipulated variable actual value, which is provided at the steering system, in order to modulate the actual variable; determining a manipulated variable deviation between the manipulated variable expected value and the manipulated variable actual value; and/or determining a setpoint-actual deviation between the setpoint variable and the corresponding actual variable; approximating the friction value based on the determined load characteristic and based on the determined manipulated variable deviation and/or the determined setpoint-actual deviation. A driver assistance system is configured to carry out the method.