Autonomous Vehicle SSD Storage With Risk-Based Data Synchronization
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Solution Overview
Problem
Existing storage solutions for autonomous vehicles inefficiently manage massive data synchronization and metadata in SSDs, leading to high costs and potential data loss during accidents, necessitating a technology to securely and efficiently store accident data.
Innovation Solution
A storage device and method that differentially processes data based on accident risk levels, using a risk tag to apply distinct storage methods for user data and metadata, including on-demand logs, internal stream IDs, and synchronized flushing, to enhance efficiency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If SSD stores data with separate management of user data and metadata for performance, then storage performance is improved, but data synchronization reliability deteriorates
Solution Approach 1:
The patent segments data into user data and metadata, managing them separately in the SSD storage structure. This allows optimized storage performance for each component while maintaining the ability to synchronize them through the risk-based storage method that ensures both are recovered together in accident scenarios.
Solution Approach 2:
The patent applies preliminary action by pre-establishing risk tags and storage methods before accidents occur. The system pre-configures different storage handling for high-risk, medium-risk, and low-risk data, ensuring that when an accident happens, the synchronization and recovery process is already prepared and can execute immediately without delay.
2Reliability
If synchronization is applied to all stored data in SSD, then data recovery reliability is improved, but storage cost and complexity increase
Solution Approach 1:
The patent applies local quality by differentiating storage treatment based on data risk levels. High-risk data receives full synchronization and robust storage handling, while medium and low-risk data receive progressively lighter treatment. This localized approach ensures critical data recovery reliability without unnecessarily complicating the entire storage system.
Solution Approach 2:
The patent applies partial action by implementing synchronization and risk-based storage management only for data that requires it (high-risk data), rather than uniformly applying the same treatment to all data. This selective approach reduces overall system complexity while maintaining reliability where it matters most.
3Productivity
If risk-based differential storage method is applied, then storage efficiency and cost are improved, but data management complexity increases
Solution Approach 1:
The patent applies parameter changes by using risk tags as parameters to differentiate storage handling. The system changes storage parameters (such as synchronization level, recovery priority, and storage location) based on the risk level of the data, enabling efficient resource allocation while managing complexity through standardized risk categories.
4Adaptability or versatility
If massive sensor data is collected for high autonomous driving level, then accident analysis capability is improved, but storage cost increases rapidly
Solution Approach 1:
The patent applies local quality to storage resources by allocating different storage treatments to different data types based on their risk levels. Critical accident-related data receives prioritized storage and synchronization, while less critical sensor data receives standard handling, thereby reducing overall storage costs while maintaining capability for high-level autonomous driving analysis.
Data Source
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AI summary
A storage device (1000) configured to receive write data from at least one processor (100-800) corresponding to an autonomous vehicle (10), includes a memory (1200) and a storage controller (1100) configured to receive, from the at least one processor (100-800), the write data and an accident risk tag corresponding to the write data, the accident risk tag corresponding to an accident risk level of the autonomous vehicle (10), where the accident risk tag corresponds to at least one of a first accident risk level corresponding to a high accident risk, a second accident risk level corresponding to an accident risk lower than the first accident risk level, and a third accident risk level corresponding to an accident risk lower than the second accident risk level, select a storage method for storing the write data on the memory (1200) based on the accident risk tag, and store the write data on the memory (1200) based on the selected storage method.