Vehicle Safety Logging With Public-Private Data Separation
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Solution Overview
Problem
Existing vehicle operation logging systems capture unstructured and unsecured data, leading to large volumes of data that are not fully useful for analysis, lack integrity protection, and expose privacy risks, with insufficient differentiation for information sensitivity and lack of simultaneous online logging capabilities.
Innovation Solution
A structured logging system with secure and private data capture, including a sensor array interface, data classification circuitry, and vehicle data interface, which separates data into public and private buckets with encryption and integrity protection, enabling real-time online logging and resistance to modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unstructured data capture is used to log all vehicle operations, then comprehensive data coverage is achieved, but data usability for analysis deteriorates due to large volumes of irrelevant information
Solution Approach 1:
The patent segments logged data into structured categories including timestamp, event type, vehicle state parameters, environmental conditions, and system responses. This segmentation transforms unstructured data streams into organized records that can be efficiently queried and analyzed, directly resolving the contradiction between data volume and usability.
Solution Approach 2:
The system extracts only relevant data elements related to safety-critical events and operational anomalies from the full data stream. By filtering and extracting only meaningful information during logging, the system reduces overall data volume while maintaining high usability for analysis, eliminating the need to process unnecessary data.
2Ease of operation
If all captured data is stored in plain format for easy access, then data accessibility is improved, but data security and privacy protection deteriorate
Solution Approach 1:
The patent applies different quality attributes to different portions of data based on sensitivity. Non-sensitive operational data is stored in accessible formats for routine analysis, while sensitive personal information and security-critical data are encrypted and access-restricted. This local differentiation resolves the contradiction by providing appropriate accessibility and security for each data type.
Solution Approach 2:
The system introduces an intermediary encryption layer between data storage and data access. Encryption keys and access controls act as intermediaries that protect sensitive data while allowing authorized access when needed. This intermediary mechanism enables both security protection and controlled accessibility simultaneously.
3Quantity of substance
If comprehensive logging is performed without structure for complete event capture, then event coverage is improved, but data integrity and verification deteriorate due to lack of validation
Solution Approach 1:
The patent implements preliminary validation rules and data verification mechanisms before data is committed to storage. Each logged event undergoes integrity checks, format validation, and consistency verification in advance. This preliminary action ensures that only valid, complete, and accurate data is stored, maintaining high integrity while achieving comprehensive event coverage.
Solution Approach 2:
The system incorporates feedback mechanisms where logged data is continuously verified against expected patterns and ranges. Validation feedback loops detect and flag anomalies, ensuring data integrity. This feedback process maintains reliability by automatically detecting and correcting issues while preserving complete event coverage.
4Productivity
If real-time online logging is implemented for immediate data capture, then logging speed is improved, but system resource consumption and complexity deteriorate
Solution Approach 1:
The patent implements periodic logging intervals for different data types based on their criticality and change frequency. High-priority safety data is logged continuously in real-time, while lower-priority operational parameters use periodic sampling. This periodic approach achieves effective real-time logging for critical events while reducing overall system complexity and resource consumption.
Data Source
AI summary
Various aspects of methods, systems, and use cases for safety logging in a vehicle are described. In an example, an approach for data logging in a vehicle includes use of logging triggers, public and private data buckets, and defined data formats, for data provided during autonomous vehicle operation. Data logging operations may be triggered in response to safety conditions, such as detecting a dangerous situation from a failure of the vehicle to comply with safety criteria of a vehicle operational safety model. Data logging operations may include logging data in response to detection of the dangerous situation, including storage of a first portion of data in a public data store, and storage of a second portion of privacy-sensitive data in a private data store, where the data stored in the private data store is encrypted, and where access to the private data store is controlled.