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
- trajdata Public Forked from NVlabs/trajdata
A unified interface to many trajectory forecasting datasets.
- MaD-PF Public Forked from ETH-PBL/MaD-PF-Code
Code for MaD-PF: Modular and Differentiable Particle Filter for Cross-Environment SE(3) LiDAR Localization
- survey-physics-embedded-robot-learning Public
This repository collects our survey paper on embedding physics priors in robot learning
- 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.
- A2RL_Dataset_website Public
- jarvis_voice_assistant Public
A voice-activated speech-to-command classifier for high-level command recognition in robotic software stacks.
- SAVANT Public
- PlannerForge Public
[EMNLP 2026]PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
- Bib-Hallucination-Checker Public
People
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