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
The main research at the Professorship Autonomous Vehicle Systems focusing on generating the next generation of intelligent autonomous vehicles
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Repositories
Showing 10 of 73 repositories
- 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.
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- A2RL_Dataset_website Public
- jarvis_voice_assistant Public
A voice-activated speech-to-command classifier for high-level command recognition in robotic software stacks.
- PlannerForge Public
[EMNLP 2026]PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
- Bib-Hallucination-Checker Public
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