From the 1 of 12 linked papers with an AI index.
12 papers
VOLA: Improving Open-World Driving by VLM-Based Semantic Attribute Prediction
Yuchen Zhang, Yuan Gao, Sebastian Schmidt +1
Driving in the real world is open-world: a car may encounter a fallen mattress, a deer, or other objects outside its training data. Naming them is not enough. The system must know…
SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing
Zhouheng Li, Fangguo Zhao, Mattia Piccinini +6
Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversit…
In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing
Qunying Song, Yuan Gao, Johannes Betz +3
Autonomous driving systems (ADS) are rapidly advancing and increasingly deployed in real-world applications. This creates growing demands for effective testing to ensure system fun…
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
Yuan Gao, Wenting Miao, Mattia Piccinini +3
Chat2Scenic is an interactive, retrieval‑augmented framework that uses large language models to generate executable scenario scripts in a domain‑specific language for testing auton…
Imagined Rollouts are Kinematic, Not Dynamic: A Diagnosis of Long-Horizon World-Model Failure
Finn Rasmus Schäfer, Korbinian Moller, Yuan Gao +3
Long-horizon failure in world models is conventionally attributed to compounding error, a generic framing that does not distinguish what kind of error compounds. We propose a kinem…
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5
While Vision-Language Models (VLMs) have advanced high-level reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains…