TY - JOUR AU - Babikian, Aren A. AU - Ficsor, Attila AU - Semeráth, Oszkár AU - Mussbacher, Gunter AU - Varró, Dániel TI - Automated and logically exhaustive generation of traffic scenarios at road junctions using a multi-level danger definition JF - SOFTWARE AND SYSTEMS MODELING J2 - SOFTW SYST MODEL PY - 2026 PG - 25 SN - 1619-1366 DO - 10.1007/s10270-026-01372-y UR - https://m2.mtmt.hu/api/publication/37253633 ID - 37253633 N1 - This paper was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation and Vetenskapsrådet (VR), by the Doctoral Excellence Fellowship Programme (DCEP) funded by the National Research Development and Innovation Fund of the Ministry of Culture and Innovation and the Budapest University of Technology and Economics, and by the Department of Navy award (N629092412063) issued by the Office of Naval Research. AB - To ensure their safe use, autonomous driving systems (ADSs) must meet rigorous safety assurance criteria that involve executing maneuvers safely within arbitrary scenarios where other actors perform their intended maneuvers. For that purpose, existing scenario generation approaches optimize search to derive scenarios with high probability of dangerous interactions. In this paper, we hypothesize that at road junctions, potential danger predominantly arises from overlapping paths of individual actors carrying out their designated high-level (abstract) maneuvers. As a step toward ADS safety assurance, we propose an approach to derive an exhaustive set of potentially dangerous logical scenarios at any given road junction, i.e., all permutations of overlapping maneuvers assigned to actors, including the ADS, for a given set of possible maneuvers. From these logical scenarios, we derive concrete-level exact paths that actors must follow to guide simulation-based testing toward potential collisions. We conduct extensive experiments over two realistic road junctions with increasing number of external actors to (1) compare our scenario generation approach to the state-of-the-art Scenic tool and to (2) evaluate the behavior of a state-of-the-art learning-based ADS controller. Results show that (1) our approach outperforms Scenic both in terms of achieved coverage and runtime, and (2) that the ADS-under-test is involved in increasing percentages of unsafe behaviors in simulation, which vary according to abstract scenario properties. LA - English DB - MTMT ER - TY - JOUR AU - Ficsor, Attila AU - Papp, Inez Anna AU - Marussy, Kristóf AU - Semeráth, Oszkár TI - From Constraints to Commands: Graph Pattern Differentiation in 4-Valued First-Order Logic JF - JOURNAL OF OBJECT TECHNOLOGY J2 - J OBJECT TECHNOL PY - 2026 SP - 1 EP - 14 PG - 14 SN - 1660-1769 UR - https://m2.mtmt.hu/api/publication/37312880 ID - 37312880 LA - English DB - MTMT ER - TY - CHAP AU - Marussy, Kristóf AU - Ficsor, Attila AU - Semeráth, Oszkár AU - Varró, Dániel TI - Refinery: Graph Solver as a Service: Refinement-based Generation and Analysis of Consistent Models T2 - ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings PB - Association for Computing Machinery (ACM) CY - New York, New York SN - 9798400702174 T3 - Proceedings - International Conference on Software Engineering, ISSN 0270-5257 PY - 2024 SP - 64 EP - 68 PG - 5 DO - 10.1145/3639478.3640045 UR - https://m2.mtmt.hu/api/publication/34872599 ID - 34872599 AB - Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https://youtu.be/Qy-3udNsWsM. Continuously deployed at: https://refinery.services/. © 2024 IEEE Computer Society. All rights reserved. LA - English DB - MTMT ER - TY - CHAP AU - Ficsor, Attila AU - Semeráth, Oszkár ED - Renczes, Balázs TI - An Initial Performance Analysis of Graph Predicate Evaluation over Partial Models T2 - Proceedings of the 29th Minisymposium of the Department of Measurement and Information Systems Budapest University of Technology and Economics PB - BME Méréstechnika és Információs Rendszerek Tanszék CY - Budapest SN - 9789634218722 PY - 2022 SP - 1 EP - 4 PG - 4 SN - 9789634218722 DO - 10.3311/MINISY2022-001 UR - https://m2.mtmt.hu/api/publication/33283704 ID - 33283704 LA - English DB - MTMT ER - TY - CONF AU - Ficsor, Attila TI - Semantic Robustness Testing for Vision-Based Machine Learning Components of Autonomous Cyber-Physical Systems T2 - The 13th Conference of PhD Students in Computer Science : June 29 – July 1, 2022 Szeged, Hungary PB - Szegedi Tudományegyetem, Informatikai Intézet C1 - Szeged PY - 2022 SP - 35 EP - 39 PG - 5 UR - https://m2.mtmt.hu/api/publication/33283734 ID - 33283734 LA - English DB - MTMT ER - TY - CONF AU - Ficsor, Attila AU - Pintér, Balázs TI - Toolchain for the Construction of Realistic Simulated Urban Environments T2 - The 13th Conference of PhD Students in Computer Science : June 29 – July 1, 2022 Szeged, Hungary PB - Szegedi Tudományegyetem, Informatikai Intézet C1 - Szeged PY - 2022 SP - 40 EP - 44 PG - 5 UR - https://m2.mtmt.hu/api/publication/33283785 ID - 33283785 LA - English DB - MTMT ER -