Vehicle Friction Coefficient Determination Using Quality Characteristics
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Solution Overview
Problem
Existing methods for determining vehicle-specific friction coefficients are inadequate for autonomous driving, as they do not account for variations in vehicle-specific factors such as tires and weight, leading to unreliable friction coefficient assessments.
Innovation Solution
A method that uses a standardized friction coefficient map and a quality characteristic, which includes a standard dimension, data sharpness, and statistical confidence index, to convert standardized friction coefficients into vehicle-specific coefficients, allowing each vehicle to adapt and learn from its unique properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a standardized friction coefficient map is used for all vehicles, then data availability and map coverage are improved, but measurement precision and reliability of vehicle-specific friction coefficients deteriorate due to variations in tires, weight, and other vehicle-specific factors
Solution Approach 1:
The patent applies parameter changes by introducing a quality characteristic that transforms standardized friction coefficients into vehicle-specific friction coefficients. This quality characteristic includes parameters such as a standard dimension representing deviation from standardized values, data sharpness indicating measurement reliability, and statistical confidence indices. By dynamically adjusting these parameters based on vehicle-specific factors like tire type, weight, and driving conditions, the system maintains high data availability from the standardized map while achieving accurate vehicle-specific friction coefficient determination.
2Reliability
If individual quality characteristics are determined for each vehicle, then reliability of vehicle-specific friction coefficients is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the quality characteristic is continuously determined and refined based on comparisons between measured friction coefficients and standardized map values. The system uses statistical confidence indices and data sharpness metrics to evaluate measurement quality, then feeds this information back to adjust the quality characteristic. This feedback loop enables reliable vehicle-specific friction coefficient determination while managing system complexity through iterative learning and adaptation rather than requiring complex initial configurations.
Solution Approach 2:
The system applies self-service by enabling each vehicle to automatically determine its own quality characteristic based on its specific properties and measurements. The vehicle independently assesses its friction coefficient measurements, compares them with standardized map data, and derives its own quality parameters including standard dimension and confidence indices. This self-determination approach reduces the need for complex centralized calibration systems while maintaining high reliability across different vehicle types.
3Quantity of substance
If friction coefficient measurements are collected from a small vehicle fleet, then data collection costs are reduced, but measurement precision and statistical reliability deteriorate
Solution Approach 1:
The patent compensates for small fleet sizes by introducing quality characteristics with multiple parameters including statistical confidence indices and data sharpness metrics. These parameters transform limited measurement data into reliable vehicle-specific friction coefficients by accounting for measurement uncertainty and variability. The standard dimension parameter adjusts standardized friction coefficients based on the specific vehicle's deviation from the fleet average, while confidence indices quantify the reliability of these adjustments, enabling precise results even with limited data from small fleets.
Data Source
AI summary
A method determines a quality characteristic, in which the quality characteristic is representative for the procurement of a vehicle-specific friction coefficient of a vehicle and a standardized friction coefficient of a friction coefficient map. The friction coefficient data are received, the friction coefficient data being representative for a friction coefficient measured depending upon the position of a vehicle and a quality characteristic representative for the vehicle. The friction coefficient map is provided, the friction coefficient being representative for standardized friction coefficients of a vehicle fleet of a route network. Depending upon the friction coefficient data and the friction coefficient map, the quality characteristic is determined again and transmitted to the vehicle.