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Found 2 papers, 1 papers with code
DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences
Recent advancements in foundation models (FMs) have unlocked new prospects in autonomous driving, yet the experimental settings of these studies are preliminary, over-simplified, and fail to capture the complexity of real-world driving scenarios in human environments.
Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
Such situated evaluation provides a more comprehensive assessment of mental states and potentially mitigates the risk of shortcuts and data leakage.
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