Ahmet Soyyigit
Papers
1
Total Citations
5
H-Index
1
About
Dr. Ahmet Soyyigit is a rising researcher in autonomous systems and robotics, with a primary focus on deep learning for real-time navigation. His most notable contribution is the development of TinyLidarNet, an end-to-end deep learning model that directly maps 2D LiDAR data to control signals for autonomous racing platforms like the F1TENTH. This work addresses a critical gap in the field: while most end-to-end navigation systems rely on cameras, TinyLidarNet demonstrates that LiDAR-based approaches can achieve robust, low-latency performance, making them ideal for resource-constrained environments. With 5 citations since its 2024 publication, this paper has already garnered attention for its practical implications in autonomous racing and mobile robotics. Dr. Soyyigit’s research pushes the boundaries of sensor-efficient AI, offering a scalable solution for vehicles operating in dynamic, obstacle-rich settings. His work is particularly relevant for students and researchers interested in merging deep learning with real-world robotic systems, and it stands as a promising foundation for future advancements in autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1