@InProceedings{Dauner2026ARXIV,
author = {Daniel Dauner and Valentin Charraut and Bastian Berle and Tianyu Li and Long Nguyen and Jiabao Wang and Changhui Jing and Maximilian Igl and Holger Caesar and Boris Ivanovic and Yiyi Liao and Andreas Geiger and Kashyap Chitta},
title = {123D: Unifying Multi-Modal Autonomous Driving Data at Scale},
booktitle = {arXiv.org},
year = {2026},
}Long Nguyen
PhD Candidate · University of Tübingen · KE:SAI · ELLIS Institute Tübingen
I am a researcher at KE:SAI, advised by Prof. Dr. Andreas Geiger in the Autonomous Vision Group at the University of Tübingen and Dr. Kashyap Chitta in the Physical AI Lab at the ELLIS Institute Tübingen.
My research focuses on end-to-end driving policies, which learn perception and planning jointly from raw sensor data. I am guided by two questions:
- How to make a policy drive better?
- How do we make that progress measurable and reproducible?
Through my training in the Autonomous Vision Group, where I am lucky to learn from people who have shaped how the field evaluates autonomous driving, I have become convinced that such progress can be measured most reliably in closed loop, which makes simulation indispensable for driving and robotics alike. I also believe in open research: when code, data, and models are shared, we can build on each other's methods, and results remain verifiable as the field moves on.
Publications
Conference on Computer Vision and Pattern Recognition (CVPR), 2026
@InProceedings{Nguyen2026CVPR,
author = {Long Nguyen and Micha Fauth and Bernhard Jaeger and Daniel Dauner and Maximilian Igl and Andreas Geiger and Kashyap Chitta},
title = {LEAD: Minimizing Learner Expert Asymmetry in End-to-End Driving},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2026},
}Education
Ph.D., Computer Science
M.Sc., Machine Learning
B.Sc., Medical Informatics
Miscellaneous
2nd Place, Vision-based End-to-End Driving Challenge