Vehicle Software Update Clustering for Network Congestion
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Solution Overview
Problem
Existing systems for updating and validating vehicle software configurations in motor vehicles face issues such as network congestion, delay, and packet loss due to the significant amount of network data transmitted to Electronic Control Units (ECUs), which current technologies do not adequately address.
Innovation Solution
A system and process that utilizes a network appliance with processors to collect data on ECU software configurations, determine clusters of vehicles with common configurations, identify optimal sets of clusters for software updates, and rank these clusters based on vehicle coverage percentage, using an interaction matrix dependency table and structural coverage tree to ensure efficient updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software updates are transmitted to all vehicles, then all vehicles receive updates, but network congestion, delay, packet loss, and jitter occur due to the significant amount of network data
Solution Approach 1:
The system segments the fleet into multiple clusters based on software configuration similarity. Instead of transmitting updates to all vehicles individually, updates are transmitted to cluster representatives or aggregated groups, reducing the total number of transmission targets while maintaining comprehensive coverage across diverse vehicle configurations.
Solution Approach 2:
The system creates a universal update mechanism that can serve multiple vehicle configurations through cluster-based aggregation. A single update package can be applied to multiple vehicles within a cluster that share common software architecture and configuration patterns, eliminating the need for individualized update transmissions for each vehicle.
2Object-affected harmful factors
If updates are transmitted to reduce network congestion, then network performance improves, but not all vehicle configurations may be adequately covered
Solution Approach 1:
The system performs preliminary clustering analysis before update transmission to identify which vehicle configurations should be grouped together. By pre-processing the fleet data to determine configuration similarities and establishing cluster relationships in advance, the system ensures that update coverage is optimized before the actual transmission occurs, preventing both congestion and coverage gaps.
Solution Approach 2:
The system implements feedback mechanisms to monitor update coverage effectiveness and network performance. Based on this feedback, the clustering algorithm can be adjusted and refined, allowing the system to learn from previous update cycles and improve both coverage and network efficiency in subsequent transmissions.
3Reliability
If all vehicle software configurations are updated individually, then complete coverage is achieved, but the complexity and time required for validation increases significantly
Solution Approach 1:
The system merges validation efforts by grouping vehicles with similar software configurations into clusters. Validation can be performed on cluster representatives or aggregated configurations rather than on each individual vehicle, significantly reducing the total validation time while maintaining confidence in comprehensive coverage through the clustering relationships.
4Object-affected harmful factors
If network data transmission is reduced, then network congestion decreases, but the precision of software configuration matching may be compromised
Solution Approach 1:
The system applies local quality by performing detailed configuration analysis and matching only for vehicles within the same or similar clusters, rather than requiring precise matching across the entire fleet. This allows for reduced data transmission while maintaining high matching accuracy within local cluster contexts, where configurations are already known to be similar.
Data Source
AI summary
A system is provided for updating vehicle software configurations for multiple vehicles. The system includes a network having multiple ECUs carried by the vehicles, with each ECU having an ECU software configuration including software components with one or more versions. The system further includes a network appliance having a processor communicating with the ECUs and a computer readable medium. The processor is programmed to identify coverage points for software components. The processor is further programmed to collect clusters for validating an update of the associated software components and determine optimal sets of clusters, which is less than or equal to a total of the clusters. The processor is further programmed to rank the clusters of the selected optimal set, based on a vehicle coverage percentage of each cluster, and transmit the optimal set for software configuration validation and then finally transmit the validated update to the associated vehicle ECUs.