Our AVS Labs research is motivated by the goal of developing the next generation of intelligent autonomous vehicles. We aim to develop autonomous vehicles that will be able to interact with each other and with humans while operating safely, efficiently, and powerfully. On Our Github page you will find resources for teaching and research. Here you will find the algorithms, tools and simulations we developed to enable safe and trustworthy autonomy for a wide range of highly integrated autonomous vehicle applications.
TUM - Autonomous Vehicle Systems Lab
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Repositories
- Chat2scenic Public
[IROS'26] Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
- A2RL_Dataset_website Public
- trajdata Public Forked from NVlabs/trajdata
A unified interface to many trajectory forecasting datasets.
- ICRA2026_Workshop Public
- target-bench Public
[ECCV'26] Official repo for Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?
- iKCE Public
iKCE: diagnosing kinematic-vs-dynamic imagination in latent world models (RSS 2026 World Model Workshop)
- FM-AD-Survey Public
[Survey Paper] This repository collects research papers of large Foundation Models for Scenario Generation and Analysis in Autonomous Driving. The repository will be continuously updated to track the latest update.
- EgoDyn-Bench Public
Official Repository for EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
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