Selim Solmaz
Assoc. Prof. Dr. Selim Solmaz obtained his B.Sc. degree from Middle East Technical University (METU) Aerospace Engineering Department in 2001, and M.Sc. degree from Purdue University-West Lafayette, the School of Aeronautics and Astronautics in 2003. Dr. Solmaz obtained his Ph.D. from Electronics Engineering department of the National University of Ireland-Maynooth (NUIM) in 2008. The Ph.D. studies of Dr. Solmaz were focused on active safety control system analysis and design for automotive vehicles. Following graduation from the Ph.D., Dr. Solmaz continued to work on this topic as a Post Doctoral Research Fellow at the Hamilton Institute in NUIM. Dr. Solmaz relocated back to Turkey in 2010 and got appointed as an Assistant Prof. at Gediz University Mechanical Engineering Department. He promoted to Associate Prof. in 2013. He then moved to Northern Cyprus in 2016 and worked in Girne American University, Energy Systems Engineering Department. He then switched to Near East University, Automotive Engineering Department in 2017 and continues to this post since then. His current research areas include electric and hybrid vehicle systems, control system design, renewable energy systems and unmanned ground and air vehicles.
Supervisors: Prof. Robert N. Shorten, Prof. Martin Corless, and Prof. Kathleen Howell
Supervisors: Prof. Robert N. Shorten, Prof. Martin Corless, and Prof. Kathleen Howell
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becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radarās perception, particularly the radar cross-section (RCS),
proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radarās perception for various vehicles and aspect angles. ABayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The modelās effectiveness is demonstrated through accurate reproduction of the RCS
behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more
extensive validation is proposed to refine accuracy and broaden the modelās applicability.
vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a
reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments.
becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radarās perception, particularly the radar cross-section (RCS),
proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radarās perception for various vehicles and aspect angles. ABayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The modelās effectiveness is demonstrated through accurate reproduction of the RCS
behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more
extensive validation is proposed to refine accuracy and broaden the modelās applicability.
vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a
reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments.