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cs.RO2026
ControlMap: Controllable High-Definition Map Generation for Traffic Scenario Simulation
Marwan Farag, Steffen Wäldele, Yu Yao
Simulation is central to validating autonomous driving systems, yet current pipelines are limited by insufficient scenario diversity due to costly High Definition (HD) map creation…
cs.RO2025
AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
Yu Yao, Salil Bhatnagar, Markus Mazzola +5
Rare, yet critical, scenarios pose a significant challenge in testing and evaluating autonomous driving planners. Relying solely on real-world driving scenes requires collecting ma…
cs.RO2025
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models
Jiawei Zhang, Xuan Yang, Taiqi Wang +3
Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors. Recent advances…