A Graph Classification Approach to Secure Compatibility of Software and Hardware Configurations in Distributed Automotive Systems University of Stuttgart

Format:

Book

Conference/Event

Author/Creator:

Wizl, Jens, author.

Guarda, Filippo, author.

Conference Name:

2026 Stuttgart International Symposium (2026-07-08 : Stuttgart, Germany)

Language:

English

Subjects (All):

Neural networks.

Architecture.

Computer software and hardware.

Local Subjects:

Neural networks.

Architecture.

Computer software and hardware.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Software-defined, highly customizable vehicle architectures drastically increase the number of hardwaresoftware constellations that must be validated, especially under safety and timing constraints. Traditional unit and integration testing, as well as current regression and combinatorial methods, cannot practically cover this configuration space or reliably capture emergent effects arising from complex interactions, such as bandwidth contention and non-linear latency behavior. This work presents a proof-of-concept for predictive, situational validation of self-describing hardware and software components within realistic automotive E/E architectures. Proposing a novel Machine Learning- (ML) based method for early systemic feasibility prediction of automotive configurations using Graph Neural Networks (GNNs). Specifically, the subclass Graph Isomorphism Networks (GINs) is applied to predict the compatibility of a randomly composed configuration of software and hardware components, assessing both structural compatibility and functional stability. The trained models achieve recall and accuracy above 90%, even when detailed behavioral metadata is hidden during training, indicating that systemic incompatibilities are learnable from topological features alone. Results were achieved from training on a realistic, synthetic dataset representing less than 10e27% of all possible permutations without finetuning or further parameter optimization. It demonstrates the potential of GIN-based graph learning to enable early, automated feasibility assessment, substantially reducing testing time and development effort for modular, personalized, and update-capable vehicle architectures

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license