Dynamic Content Data Verification for Autonomous Driving
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Solution Overview
Problem
Autonomous driving systems face challenges in verifying large-size content data, such as map data, in real-time due to the risk of cyber attacks and the time-consuming nature of conventional verification methods, which can lead to the use of invalid data.
Innovation Solution
A verification method that divides content data into partial data sets, generates hash values, and uses a signature to verify the data by selecting a subset of partial data based on vehicle state information, allowing for quick verification while maintaining security, by determining an integer N greater than or equal to one based on the state information and using it to generate second hash values for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the whole content data is verified using conventional verification methods, then the verification reliability is improved, but the verification time increases significantly
Solution Approach 1:
The content data is divided into multiple pieces of partial data, and verification is performed on selected partial data rather than the entire dataset. This segmentation allows the system to verify data faster by focusing on representative portions while maintaining reliability through strategic selection of verification targets based on vehicle state.
Solution Approach 2:
Instead of verifying all content data, the system performs partial verification on N selected pieces of partial data where N is determined based on vehicle state information. This partial action approach achieves sufficient verification reliability for real-time autonomous driving needs without the time cost of complete verification.
2Reliability
If conventional verification methods are used on large-size content data, then data security is improved, but real-time usability is lost
Solution Approach 1:
The verification process is made dynamic by adjusting the number of partial data pieces (N) to be verified based on real-time vehicle state information. This dynamic adaptation allows the system to balance security and speed according to current operational conditions, enabling real-time usability while maintaining appropriate security levels.
Solution Approach 2:
The system changes the verification parameter (number of partial data pieces N) based on vehicle state parameters such as autonomous driving execution status and map data usage status. This parameter adjustment enables the system to optimize between security and real-time performance based on actual operational context.
3Measurement precision
If the integer N is increased to verify more partial data, then verification accuracy is improved, but processing speed decreases
Solution Approach 1:
The value of N (number of partial data pieces to verify) is dynamically determined based on vehicle state information rather than being fixed. This allows the system to adjust verification accuracy and processing speed according to real-time needs, achieving optimal balance between the two competing requirements.
Data Source
AI summary
A method for verifying content data to be used in a vehicle is provided. The method includes acquiring content data, acquiring, from partial data divided from the content data, a respective plurality of first hash values, acquiring a signature generated by using the first hash values and a key, acquiring state information that indicates a state of a vehicle, determining an integer N that is greater than or equal to one based on the acquired state information, generating, from N pieces of partial data included in the partial data, respective second hash values, verifying the content data by using each of (a) a subset of the plurality of first hash values respectively generated from partial data other than the N pieces of partial data, (b) the second hash values, and (c) the signature, and outputting information that indicates a result of the verifying.