Long Nguyen · PhD Candidate, University of Tübingen · KE:SAI

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. Two simple questions guide my work:

What do imitation-learned policies really need to excel in closed loop?
What makes a driving benchmark predictive of real-world performance?

Trained in a group that has shaped how the field evaluates autonomous driving, I am convinced that such progress is 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 work, and results remain verifiable as the field moves on.

I am always open to collaborations on policy learning, closed-loop benchmarking, and driving simulation. The best way to reach me is by email or LinkedIn.

Research interests Policy learning · Closed-loop benchmarking · Representation learning

News

Oct 2026py123d_garage v0.2 released. Blog post Driving Made Simple: A Fully Open Recipe from Public Data.
Aug 2026DriveIRT released together with the NavHard-IRT leaderboard.
Jun 2026LEAD presented at CVPR 2026. Blog post No More Shortcuts: Closed-Loop Driving Without Target Point Bias.
May 2026Started my PhD journey, advised by Andreas Geiger and Kashyap Chitta.
May 2026123D preprint released together with the py123d library.

Dec 2025LEAD preprint on arXiv.
Jun 20252nd place in the Waymo Open Dataset Vision-based End-to-End Driving Challenge.

Publications

123D: Unifying Multi-Modal Autonomous Driving Data at Scale

Daniel Dauner, Valentin Charraut, Bastian Berle, Tianyu Li, Long Nguyen, Jiabao Wang, Changhui Jing, Maximilian Igl, Holger Caesar, Boris Ivanovic, Yiyi Liao, Andreas Geiger, Kashyap Chitta

arXiv.org, 2026

Project Page Paper Code

@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}, 
}

LEAD: Minimizing Learner Expert Asymmetry in End-to-End Driving

Long Nguyen, Micha Fauth, Bernhard Jaeger, Daniel Dauner, Maximilian Igl, Andreas Geiger, Kashyap Chitta

Conference on Computer Vision and Pattern Recognition (CVPR), 2026

Project Page Paper Supplemental Code Blog

@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}, 
}