17 citations · 17 across the 15 of their papers we have counts for
15 papers
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
Yuan Gao, Sebastian Müller, Mattia Piccinini +5
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains…
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 increasingly deployed in complex real-world environments, creating strong demands for rigorous testing to establish their functionality and saf…
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
Yuan Gao, Wenting Miao, Mattia Piccinini +3
Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatic…
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…
StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
Yuan Gao, Dengyuan Hua, Mattia Piccinini +4
Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which tra…
Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?
Dingrui Wang, Zhihao Liang, Hongyuan Ye +13
While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target…
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
Roberto Brusnicki, David Pop, Yuan Gao +2
Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution semantic anomalies. While VLMs have emerged as promising tools for perception,…
NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving
Yuan Gao, Mattia Piccinini, Roberto Brusnicki +2
Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Model…
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang +12
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing hav…
From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
Yuan Gao, Mattia Piccinini, Korbinian Moller +2
Ensuring the safety of autonomous vehicles requires virtual scenario-based testing, which depends on the robust evaluation and generation of safety-critical scenarios. So far, rese…
H2 suboptimal containment control of homogeneous and heterogeneous multi-agent systems
Yuan Gao, Junjie Jiao, Zhongkui Li +1
This paper deals with the H2 suboptimal state containment control problem for homogeneous linear multi-agent systems and the H2 suboptimal output containment control problem for he…