Adaptive Acceleration Thresholds for Driver-Dependent Event Recording
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Solution Overview
Problem
Existing event data recorders inaccurately assess dangerous situations, recording non-threatening events and failing to detect actual dangers, due to reliance on simple acceleration thresholds and noise or disturbances, and may incorrectly identify dangerous situations for inexperienced drivers.
Innovation Solution
An information processing method that acquires and analyzes images of a vehicle's interior to identify the driver, assess their experience level based on attributes and history, and adjust acceleration thresholds for recording outside vehicle images, ensuring only abnormal situations are recorded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed acceleration threshold is used for detecting dangerous situations, then the detection method is simple and easy to implement, but it causes false detection of non-dangerous situations and misses actual dangerous situations due to noise and disturbances
Solution Approach 1:
The patent applies dynamics by making the acceleration threshold adaptive rather than fixed. The threshold dynamically changes based on the driver's experience level (assessed through image recognition) and current driving conditions. This allows the system to adjust sensitivity in real-time, reducing false detections for inexperienced drivers while maintaining high detection accuracy for actual dangerous situations.
Solution Approach 2:
The patent changes the threshold parameter based on driver characteristics and driving conditions. By assessing driver experience level through image analysis and modifying the acceleration threshold accordingly, the system optimizes detection accuracy. Additionally, the threshold is adjusted based on vehicle speed and other environmental factors, making the detection system more reliable without requiring complex hardware modifications.
2Measurement precision
If image analysis is added to assess driver experience level, then the detection accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent applies preliminary action by performing driver assessment and threshold adjustment before a dangerous situation occurs. The system continuously monitors driver characteristics through image recognition and pre-calculates appropriate thresholds based on assessed experience levels. This preparation ensures that when a dangerous situation arises, the detection can occur immediately using pre-determined thresholds, minimizing processing delays.
Solution Approach 2:
The patent uses lightweight image processing techniques that require minimal computational resources. Instead of complex continuous analysis, the system uses periodic snapshot assessment of driver images to determine experience level, then applies the determined threshold for extended periods. This approach achieves accurate driver assessment without requiring heavy computational power or continuous processing.
Data Source
AI summary
A computer implemented information processing method includes acquiring a first photographed image that represents an inside portion of a vehicle, identifying a driver who drives the vehicle from the first photographed image, acquiring driver information that indicates at least one of an attribute of the identified driver and a driving history of the identified driver. Then the method determines whether or not the identified driver satisfies a condition of experienced driver using the driver information. Finally the method decides a threshold value that is used for detection of occurrence of an event to be a trigger of photographing or recording of a second photographed image which represents an outside portion of the vehicle based on a result of the determination.