Vehicle Fleet Update Prioritization by Configuration Similarity
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Solution Overview
Problem
Managing software updates across fleets of vehicles is resource-intensive and challenging due to the need for manual planning and prioritization, often leaving vehicles without timely updates to address security threats and safety issues.
Innovation Solution
A method that uses a recommendation system to analyze the hardware and software configurations of vehicles, calculating similarity scores to prioritize updates based on similarities with a reference vehicle, focusing on specific attack paths and vulnerabilities to efficiently distribute software updates, firmware updates, and security patches across the fleet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and planning is used for software updates across vehicle fleets, then update accuracy and security can be ensured, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent creates a digital twin (virtual model) of the vehicle fleet that replicates hardware configurations, software versions, and attack surfaces. This virtual model allows automated analysis and prioritization of security updates without requiring manual review of each vehicle, thus maintaining security while dramatically reducing planning time.
Solution Approach 2:
The patent introduces an automated recommendation system that acts as an intermediary between security threats and fleet update management. This system analyzes attack paths, calculates risk scores, and generates prioritized update recommendations, eliminating the need for manual planning while ensuring security through systematic analysis.
2Reliability
If comprehensive security analysis is performed across all vehicles in a fleet, then security vulnerabilities can be identified, but the computational resources and time required become prohibitively high
Solution Approach 1:
The patent applies local quality by focusing security analysis on specific attack paths and vulnerable components rather than performing blanket analysis across all vehicle systems. The recommendation system identifies and prioritizes critical attack surfaces, allocating computational resources only where security risks exist, thus reducing overall resource consumption while maintaining detection effectiveness.
Solution Approach 2:
The patent segments the fleet into groups based on hardware configurations, software versions, and attack surface similarities. This segmentation allows the system to analyze representative samples from each segment rather than every individual vehicle, significantly reducing computational requirements while maintaining comprehensive security coverage.
3Productivity
If software updates are deployed to all vehicles simultaneously, then security patches can be applied quickly, but system stability and rollback capability are compromised
Solution Approach 1:
The patent performs preliminary actions by generating and validating update recommendations in the virtual model before deploying to actual vehicles. The system prioritizes updates based on risk scores and attack path analysis, preparing rollback plans in advance, thus enabling rapid deployment while maintaining system stability through pre-validation and staged rollout.
Solution Approach 2:
The patent implements dynamic update deployment by adjusting rollout strategies based on real-time feedback from the fleet. The recommendation system monitors update performance and can dynamically modify deployment pace, pause updates if issues arise, or adjust prioritization based on emerging threats, thus balancing deployment speed with system stability.
Data Source
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AI summary
Mechanisms and methods are provided for establishing vectors indicating the presence, in a first vehicle and second vehicle, of a super-set of vehicle features present across a fleet of vehicles. The first vehicle may be a reference vehicle. A distance function of the vectors may be calculated in order to establish a similarity score indicating the degree of similarity between the designs of the two vehicles. If the second vehicle is sufficiently similar to the reference vehicle, a software update may be recommended and applied.