Adaptive Threshold Data Recorder for Critical Driving Situations
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Solution Overview
Problem
Existing data recorders and driver assistance systems face challenges in accurately identifying critical driving situations due to the difficulty in setting optimal threshold values, leading to either excessive or insufficient data storage and potentially dangerous imprecise identifications.
Innovation Solution
A data recorder and driver assistance system that automatically adapts threshold values based on continuously measured parameters and individual driver behavior, ensuring that only relevant critical driving situations are recorded and addressed, using a dynamically calculated threshold system that adjusts to the driver's behavior over time and distance, and allows for the consideration of different driving styles and skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed threshold values are used for triggering data recording, then the system structure remains simple, but the identification accuracy of critical driving situations deteriorates due to difficulty in optimal threshold selection
Solution Approach 1:
The patent applies dynamics by transitioning from fixed threshold values to dynamically adaptive thresholds that automatically adjust based on measured driving parameters. The system continuously monitors driving behavior and adapts threshold values in real-time, allowing the recording trigger to respond flexibly to different driving styles and conditions, thereby improving identification accuracy without significantly increasing system complexity
Solution Approach 2:
The patent implements parameter changes by modifying the threshold values based on observed driving patterns. The system changes the threshold parameters adaptively according to the driver's behavior characteristics, such as adjusting thresholds for acceleration, braking, and steering inputs based on historical data,ไป่ resolving the contradiction between simple system structure and accurate critical situation identification
2Reliability
If low threshold values are used for triggering recording, then more critical situations are captured, but storage space is depleted too quickly reducing system reliability
Solution Approach 1:
The system dynamically adjusts threshold parameters based on driving behavior analysis, changing them adaptively to maintain an optimal balance between capturing critical situations and preserving storage capacity. This prevents both premature storage depletion and loss of important data
Solution Approach 2:
The system employs feedback mechanisms where recorded driving data is continuously analyzed to refine threshold values. This feedback loop ensures that the threshold settings remain optimal over time, capturing genuine critical situations while avoiding unnecessary recordings that would waste storage space, thus maintaining both data availability and storage efficiency
3Quantity of substance
If high threshold values are used for triggering recording, then storage space is preserved, but critical driving situations may not be recorded reducing measurement precision
Solution Approach 1:
The system transitions from static high thresholds to dynamic thresholds that adapt to individual driving styles. By continuously learning from driving patterns, the system adjusts thresholds to be sensitive enough to capture genuine critical situations while remaining selective enough to preserve storage space, resolving the contradiction between storage preservation and accurate identification
4Adaptability or versatility
If threshold values are manually adjusted, then adaptation to individual drivers is possible, but the complexity of threshold selection and adjustment increases significantly
Solution Approach 1:
The system implements self-service by automatically adapting threshold values to individual drivers without requiring manual intervention. The system autonomously analyzes driving behavior patterns and adjusts thresholds accordingly, eliminating the complexity of manual threshold selection while maintaining high adaptability to different driving styles and skill levels
Solution Approach 2:
Through continuous feedback from sensor data and driving behavior analysis, the system automatically refines threshold settings for each driver. This feedback-driven adaptation process achieves personalized threshold optimization without requiring complex manual adjustment procedures, resolving the contradiction between adaptability and adjustment complexity
Data Source
Figure 1
AI summary
The recorder (10) has a triggering unit (26) to trigger recording of measuring data of a sensor system (12) when a measuring data measured by the sensor system has a threshold value defining the measuring data and/or a combination of the measured data of the associated threshold value for identifying a critical driving situation. The measuring data is relevant to a driving situation. The threshold value is automatically adapted to average driver characteristics in dependent of the measured measuring data. An independent claim is also included for a method for identification of critical driving situations and/or critical conditions, changes and events from and at a component of a motor vehicle and/or motor vehicle trailer.