6 citations · 6 across the 3 of their papers we have counts for
6 papers
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu +4
Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable. Autonomous…
Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
Glenn Jocher, Jing Qiu, Mengyu Liu +3
Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware. The YOLO family has become widely deployed for this reason, yet most YOL…
TopoMaskV3: 3D Mask Head with Dense Offset and Height Predictions for Road Topology Understanding
Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Mask-based paradigms for road topology understanding, such as TopoMaskV2, offer a complementary alternative to query-based methods by generating centerlines via a dense rasterized…
TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding
Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Understanding road topology is crucial for autonomous driving. This paper introduces TopoBDA (Topology with Bezier Deformable Attention), a novel approach that enhances road topolo…
GLane3D : Detecting Lanes with Graph of 3D Keypoints
Halil Ä°brahim Ãztürk, Muhammet Esat KalfaoÄlu, Ozsel Kilinc
Accurate and efficient lane detection in 3D space is essential for autonomous driving systems, where robust generalization is the foremost requirement for 3D lane detection algorit…
TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem
M. Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc +1
Recently, the centerline has become a popular representation of lanes due to its advantages in solving the road topology problem. To enhance centerline prediction, we have develope…