Road Capability Sensing With Selective Friction Determination
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Solution Overview
Problem
Existing methods for determining road surface friction coefficients, such as US 2015/0224925 A1, often provide unreliable results unless vehicle dynamics are altered, which may not occur when needed, leading to unnecessary and potentially undesirable changes in vehicle operation.
Innovation Solution
A method that uses a sensor to generate an information package based on road surface signals, comparing reference and previous content measures to determine if a change in road conditions has occurred, allowing the determination of road capability parameters only when significant differences are detected, thereby optimizing when to execute the determination procedure and minimizing unnecessary vehicle dynamics changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the determination procedure is executed frequently to obtain reliable road capability parameters, then measurement precision is improved, but vehicle operation stability deteriorates due to unnecessary dynamics changes
Solution Approach 1:
The system uses feedback from sensor data (color images, histograms of oriented gradients) to monitor road condition changes and triggers the determination procedure only when significant changes are detected. This feedback mechanism ensures reliable parameter determination while avoiding unnecessary vehicle dynamics changes, resolving the contradiction between measurement precision and operation stability.
Solution Approach 2:
The system monitors changes in sensor parameters (color information, gradient histograms) to detect road condition variations. By triggering the determination procedure based on parameter changes rather than fixed time intervals, the system achieves reliable parameter determination only when necessary, preventing unnecessary vehicle operation disruptions.
2Measurement precision
If vehicle dynamics are changed to determine local coefficient of friction, then measurement precision is improved, but loss of energy increases due to unnecessary acceleration or deceleration
Solution Approach 1:
The system uses feedback from sensor data to determine whether road condition changes warrant a friction measurement. By triggering vehicle dynamics changes only when sensor parameters indicate actual road condition variations, the system achieves reliable friction coefficient determination while minimizing unnecessary energy loss from acceleration or deceleration.
Solution Approach 2:
Instead of continuously changing vehicle dynamics to determine friction coefficients, the system applies partial action by triggering dynamics changes only when sensor data indicates necessary measurements. This selective approach maintains measurement precision while significantly reducing energy loss from unnecessary vehicle maneuvers.
3Productivity
If the determination procedure is executed continuously, then productivity is improved, but loss of time increases due to processing redundant data
Solution Approach 1:
The system uses feedback from sensor data analysis to intelligently trigger the determination procedure only when road condition changes are detected. This feedback-based triggering maintains high productivity by ensuring frequent measurements when needed while avoiding time-wasting processing of redundant data during stable road conditions.
Solution Approach 2:
The system applies partial action by executing the determination procedure selectively rather than continuously. By processing data only when sensor parameters indicate meaningful changes, the system maintains high productivity through adequate measurement frequency while minimizing time loss from redundant data processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach ensures that road capability parameters are determined only when necessary, improving the reliability of the knowledge base and reducing unnecessary changes in vehicle operation, such as torque application, by leveraging differences in road conditions indicated by changes in sensor data like color images and histograms of oriented gradients.
Implementation Method 1
a sensor adapted to generate an information package on the basis of signals reflected from the surface of a portion of the road segment
Data Source
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AI summary
The invention relates to a method for determining a parameter indicative of a road capability of a road segment (16) supporting a vehicle (10). The vehicle (10) comprises a sensor (18) adapted to generate an information package on the basis of signals (20, 22) reflected from the surface of a portion of the road segment (16) in front of the vehicle (10), as seen in an intended direction of travel of the vehicle (10). The vehicle (10) further comprises a plurality of ground engaging members (12, 14). The method comprises: - determining a reference content measure indicative of the information content of an information package supplied by the sensor (18) at a reference time instant (T1); - comparing the reference content measure to a previous content measure indicative of the information content of an information package supplied by the sensor (18) at a previous time instant (T0), the previous time instant (T0) occurring before the reference time instant (T1); - upon detecting that a difference between the reference content measure and the previous content measure is outside a predetermined tolerance range, allowing the execution of a determination procedure for determining the parameter indicative of the road capability of the road segment (16) supporting the vehicle (10); - upon detecting that the difference between the reference content measure and the previous content measure is within the predetermined tolerance range, not allowing the execution of the determination procedure, and - wherein the determination procedure comprises modifying the operation of one or more of the ground engaging members (12, 14).