Sander Abraham
Papers
1
Total Citations
4
H-Index
1
About
Sander Abraham is a researcher in computer vision and autonomous systems, with a focus on enhancing perception for self-driving vehicles and delivery robots. His most-cited work, “A Robust Pedestrian and Cyclist Detection Method Using Thermal Images” (2021), addresses a critical gap in autonomous navigation: the limitations of RGB-based detection in low-light or adverse weather conditions. By leveraging thermal imaging, Abraham’s approach improves the reliability of detecting vulnerable road users, offering a more robust solution for real-world deployment. This work has garnered 4 citations, reflecting its niche but growing influence in the field of thermal vision for autonomous systems. Abraham’s contributions lie at the intersection of sensor fusion and safety-critical AI, where his methods aim to reduce false negatives in pedestrian and cyclist detection—a key challenge for urban autonomous mobility. His research underscores the importance of multimodal sensing, and his thermal detection framework serves as a foundation for further exploration into non-visible spectrum perception. For students and researchers in autonomous vehicle safety, Abraham’s work offers a practical, data-driven path toward more resilient and trustworthy perception systems.
Research Focus
Key Achievements
Top Papers
- 1A Robust Pedestrian and Cyclist Detection Method Using Thermal Images4 citations · 2021