Jakob Geyer

Papers

Total Citations

265

H-Index

About

Jakob Geyer is a leading researcher in autonomous driving, machine learning, and mobile robotics, best known for his foundational contributions to the development of large-scale, high-quality datasets for self-driving vehicles. His most impactful work, the "A2D2: Audi Autonomous Driving Dataset" (2020), has garnered over 265 citations and provides the research community with a comprehensive resource of synchronized images and 3D point clouds, complete with 3D bounding box annotations. This dataset has become a critical benchmark for advancing perception algorithms, enabling more robust object detection and scene understanding in real-world driving environments. Geyer’s efforts have directly accelerated progress in autonomous vehicle technology by addressing the pressing need for diverse, annotated data. His work exemplifies the synergy between rigorous data curation and practical deployment, making him a key figure in bridging the gap between research and industry applications. Through A2D2, Geyer has empowered countless researchers and engineers to push the boundaries of safe, reliable autonomous systems.