Driver Observation Data Abstraction for Privacy-Safe Mode Transitions
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Solution Overview
Problem
Existing automatic driving systems face challenges in ensuring driver safety during mode transitions from automatic to manual driving, particularly when the driver's arousal level is low, and there are strict regulations against recording personal information that could aid in determining liability in accidents.
Innovation Solution
An information processing device that acquires driver observation information, divides it into conversion unnecessary and necessary data, and applies abstraction or encryption processing to store data in compliance with personal information protection regulations, while calculating a driver evaluation value to determine safe manual driving transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver observation information is recorded to ensure safe mode transitions, then driver safety is improved, but personal information protection regulations are violated
Solution Approach 1:
The patent extracts only the necessary driver state information (arousal level, consciousness state) from the complete driver observation data, separating it from identifiable personal information. This allows safety monitoring while complying with privacy regulations by recording only what is essential for determining whether the driver can safely transition to manual driving.
Solution Approach 2:
The patent introduces an intermediary processing step that transforms raw driver observation information into anonymized or abstracted data representations. This intermediary layer enables the system to maintain driver safety monitoring capabilities while the transformed data no longer constitutes protected personal information under regulations like GDPR.
2Loss of information
If complete driver observation data is recorded to provide effective evidence for accident liability, then evidence effectiveness is improved, but data protection compliance deteriorates
Solution Approach 1:
The patent extracts and retains only the critical safety-relevant features from driver observation data (such as arousal level indicators and consciousness state metrics) that are sufficient for accident liability determination, while excluding or anonymizing personally identifiable information. This extraction approach maintains evidentiary value while ensuring regulatory compliance.
3Reliability
If driver arousal monitoring is implemented during automatic driving, then mode transition safety is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the driver monitoring system automatically assesses arousal levels and consciousness states without requiring manual intervention or complex external validation. The system autonomously determines whether the driver is fit for manual driving transitions, reducing operational complexity while maintaining safety standards.
Data Source
AI summary
Individually-identifiable data included in observation information of a driver or the like of a vehicle or data for which recording processing is not permitted in a personal information protection regulation is abstracted or encrypted and recorded. A driver information acquisition unit that acquires the observation information of the driver of the vehicle and a data processing unit that inputs the observation information and executes data processing are included. The data processing unit divides the observation information into conversion unnecessary data and conversion necessary data, executes abstraction processing or encryption processing for the conversion necessary data, and stores conversion data such as abstraction data or encryption data in a storage unit. The data processing unit executes the abstraction processing or the encryption processing for the individually-identifiable data included in the observation information or the data for which recording processing is not permitted in the personal information protection regulation as the conversion necessary data.