Bakhbyergyen Yerjan
Papers
1
Total Citations
5
H-Index
1
About
Dr. Bakhbyergyen Yerjan is a rising researcher in autonomous systems and robotics, with a focus on end-to-end deep learning for real-time navigation. Their key contributions center on developing efficient, sensor-specific models that bypass traditional perception pipelines, directly mapping raw data to control commands. Their most cited work, "TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing" (2024, 5 citations), addresses a critical gap in the field: while most end-to-end navigation solutions rely on cameras, LiDAR remains underexplored. By introducing a compact neural network tailored for 2D LiDAR data, Dr. Yerjan demonstrates that lightweight, sensor-appropriate architectures can achieve competitive performance in high-speed racing scenarios, such as the F1TENTH competition. This work not only advances the practical deployment of LiDAR in autonomous racing but also opens avenues for resource-constrained platforms. Though early in their career, Dr. Yerjan’s research signals a promising trajectory toward more robust, sensor-diverse autonomous navigation systems, with potential applications in robotics, autonomous vehicles, and embedded AI.
Research Focus
Key Achievements
Top Papers
- 1