The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remarkable success in computer vision and natural language processing, robotics remains constrained by limited data, complex real-world interacti...
Mattia Piccinini, L. Schulze, Alice Plebe et al.· 0 citations
The results show that VLM image tokens provide useful semantic cues for transferring driving attributes to objects outside the training vocabulary, and this model matches strong vision-only segmenters on familiar categories and improves transfer to real open-world anomalies.
Yu-Chen Zhang, Yuan Gao, Sebastian Schmidt et al.· 0 citations
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 a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by s...
Yuan Gao, Sebastian Müller, Mattia Piccinini et al.· 0 citations
This study provides a comprehensive industry-grounded overview of ADS testing, proposes an evidence-centered closed-loop testing framework to provide actionable guidance for ADS testing, and outlines important directions for future research and practice.
Qunying Song, Yuan Gao, Johannes Betz et al.· arXiv.org· 0 citations
Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such scripts from regulatory descriptions remains an open challenge, and existing approaches face fundamental trade-offs. Re...
Yuan Gao, Wenting Miao, Mattia Piccinini et al.· 0 citations
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