3 papers
cs.LG2025
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation
Boqi Chen, Kristóf Marussy, Oszkár Semeráth +2
Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbat…
cs.SE2024
Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition
Aren A. Babikian, Attila Ficsor, Oszkár Semeráth +2
To ensure their safe use, autonomous vehicles (AVs) must meet rigorous certification criteria that involve executing maneuvers safely within (arbitrary) scenarios where other actor…
cs.SE2024
Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search
Aren A. Babikian, Oszkár Semeráth, Dániel Varró
Existing safety assurance approaches for autonomous vehicles (AVs) perform system-level safety evaluation by placing the AV-under-test in challenging traffic scenarios captured by…