Sevimli, Rasim A.Ucuncu, MuratKoc, Aykut2024-05-062024-05-0620232475-1472http://hdl.handle.net/11727/12054The need for visual perception systems providing situational awareness to autonomous vehicles has grown significantly. While traditional deep neural networks are effective for solving 2-D Euclidean problems, point cloud analysis, particularly for radar data, contains unique challenges because of the irregular geometry of point clouds. This letter proposes a novel transformer-based architecture for radar point clouds adapted to the graph signal processing (GSP) framework, designed to handle non-Euclidean and irregular signal structures. We provide experimental results by using well-established benchmarks on the nuScenes and RadarScenes datasets to validate our proposed method.enginfo:eu-repo/semantics/closedAccessSensor applicationsautomotive RADARgraph signal processing (GSP)point cloud processingsegmentationtransformersarticle7110011044648000072-s2.0-85176308753