Publications
Q. Goss and M. ˙I. Akbas¸, “Information Theory Based Quantitative Complexity Analysis for Autonomous Vehicle Safety Testing,” accepted to 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), IEEE, August 2025.
Details DownloadQ. Goss, W. C. Pate, and M. ˙I. Akbas¸, “An Integrated Framework for Scenario-Based Safety Validation and Explainability of Autonomous Vehicles,” ACM J. Auton. Transport. Syst., June 2025. Just Accepted.
Details DownloadM. Issler, Q. Goss, and M. ˙I. Akbas¸, “Complexity Evaluation of Test Scenarios for Autonomous Vehicle Safety Validation Using Information Theory,” Information, vol. 15, p. 772, Dec. 2024.
Details DownloadI. Kutlu, Q. Goss, T. C. Akinci, and M. ˙I. Akbas¸, “Formal Modeling of Road Network-Based Autonomous Vehicle Validation Scenarios With Intersections and Pedestrians,” in 2024 IEEE International performance Computing and Communications Conference (IPCCC), pp. 22-24, IEEE.
Details DownloadQ. Goss, W. C. Pate, and M. ˙I. Akbas¸, “An Integrated Scenario-Based Testing and Explanation Framework for Autonomous Vehicles,” in 2024 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST), pp. 01–03, IEEE.
Details DownloadM. Malayjerdi, Q. A. Goss, M. I. Akbas ̧, R. Sell, and M. Bellone,“A Two-Layered Approach for the Validation of an Operational Autonomous Shuttle,” IEEE Access, vol. 11, pp. 89124–89137, Aug. 2023.
Details DownloadJ. M. Thompson, Q. Goss, and M. I. Akbas ̧, “A Strategy for Boundary Adherence and Exploration in Black-Box Testing of Autonomous Vehicles,” in 2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST), pp. 17–19, IEEE.
Details DownloadQ. Goss and M. I. Akbas ̧, “Integration of Formal Specification and Traffic Simulation for Scenario-Based Validation,” in 2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST), pp. 17–19, IEEE.
Details DownloadJ. M. Thompson, Q. Goss, and M. I. Akbas ̧, “Boundary Adherence and Exploration in High Dimensions for Validation of Black-Box Systems,” in 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), pp. 2022–11, IEEE.
Details DownloadQ. Goss and M. I. Akbas ̧, “Eagle Strategy with Local Search for Scenario Based Validation of Autonomous Vehicles,” in 2022 International Conference on Connected Vehicle and Expo (ICCVE), pp. 07–09, IEEE.
Details DownloadA. Chakeri, X. Wang, Q. Goss, M. I. Akbas, and L. G. Jaimes, “A Platform-Based Incentive Mechanism for Autonomous Vehicle Crowdsensing,” IEEE Open J. Intell. Transp. Syst., vol. 2, pp. 13–23, Feb. 2021.
Details DownloadQ. Goss, Y. AlRashidi, and M. I. Akbas ̧, “Generation of Modular and Measurable Validation Scenarios for Autonomous Vehicles Using Accident Data,” in 2021 IEEE Intelligent Vehicles Symposium (IV), pp. 11–17, IEEE.
Details DownloadX. Wang, Q. Goss, M. I. Akbas ̧, A. Chakeri, J. M. Calderon, and L. G. Jaimes, “Incentive Mechanism for Vehicular Crowdsensing with Budget Constrains,” in 2020 SoutheastCon, pp. 28–29, IEEE.
Details DownloadQ. Goss, M. I. Akbas ̧, A. Chakeri, and L. G. Jaimes, “An Association-Rules Learning Approach to Unsupervised Classification of StreetNetworks,” in 2020 SoutheastCon, pp. 28–29, IEEE.
Details DownloadQ. Goss, M. I. Akbas ̧, L. G. Jaimes, and R. Sanchez-Arias, “Street Network Generation with Adjustable Complexity Using k-Means Clustering,” in 2019 SoutheastCon, pp. 11–14, IEEE.
Details Download