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
- PlannerForge Public
[EMNLP 2026]PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
- Bib-Hallucination-Checker Public
- VehicleDynamicsPerformanceAnalysis Public
Vehicle Dynamics Performance Analysis converts heterogeneous vehicle logs into a common CSV representation for hassle-free further processing, derives a long list of helpful vehicle-dynamics signals, slices laps, applies smoothing and offers automated PDF run reports and plotting GUI.
- 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.
- trajdata Public Forked from NVlabs/trajdata
A unified interface to many trajectory forecasting datasets.
- Chat2scenic Public
[IROS'26] Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
- A2RL_Dataset_website Public
- ICRA2026_Workshop Public
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