Vehicle Combination Yaw Instability Detection by Weighted Probabilities
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Solution Overview
Problem
Multi-unit vehicle combinations are prone to yaw instabilities such as jack-knifing and trailer swing, exacerbated by the use of electric motors and regenerative braking systems, necessitating accurate detection methods to prevent accidents.
Innovation Solution
A method involving the monitoring of multiple parameters to determine probability values for yaw instabilities, which are weighted and combined to provide a comprehensive assessment of the vehicle's stability, using reference and current values from sensors and models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single parameter is used to detect yaw instability, then the detection method is simple, but the reliability and accuracy of detection is insufficient
Solution Approach 1:
The detection system is segmented into multiple independent parameter monitoring channels (yaw rate, articulation angle, longitudinal slip, sideslip angle, understeering gradient), each evaluating a specific aspect of vehicle stability. This segmentation allows comprehensive coverage of different instability modes while maintaining modularity in the detection architecture.
Solution Approach 2:
Multiple probability values from different parameter monitoring channels are merged into a single combined probability value through weighted summation. This combining process integrates information from various sensors and parameters to produce a comprehensive stability assessment, resolving the contradiction between simple detection and reliable detection.
2Measurement precision
If multiple parameters are monitored to improve detection accuracy, then the reliability of yaw instability detection is enhanced, but the complexity of the detection system increases
Solution Approach 1:
The system transforms physical parameters (yaw rate, articulation angle, slip, sideslip angle, understeering gradient) into a unified probability scale through mathematical transformations. This parameter change enables direct comparison and combination of diverse sensor data, improving measurement precision while managing complexity through standardized processing.
Solution Approach 2:
The system continuously monitors multiple parameters and provides feedback through the probability calculation mechanism. Each parameter's deviation from safe operating conditions contributes to the overall probability value, creating a feedback loop that enhances detection precision by integrating information from multiple sources rather than relying on a single parameter.
3Reliability
If probability values from different parameters are equally weighted, then the calculation is simple, but the accuracy prediction is reduced
Solution Approach 1:
Different weights are assigned to different parameter probability values based on their individual reliability and relevance to specific instability modes. This local quality approach allows the system to emphasize more reliable or critical parameters (such as yaw rate for jack-knife detection or articulation angle for trailer swing detection) while de-emphasizing less reliable parameters, thereby improving overall prediction accuracy.
Data Source
AI summary
A method detects a yaw instability in a vehicle combination having a tractor unit and at least one trailing unit. The method includes determining a plurality of probability values each representing the probability of a yaw instability for the vehicle combination, wherein each probability value is based on a reference value and a current value of a respective parameter, applying a respective weight to each probability value, and determining a combined probability value representing the probability of a yaw instability occurring in the vehicle combination based on the weighted probability values.