AI RESEARCH
AD-H: Language-guided Autonomous Driving with Hierarchical Agents
arXiv CS.CV
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ArXi:2406.03474v2 Announce Type: replace Language-guided autonomous driving requires bridging a large abstraction gap between high-level natural-language instructions and low-level vehicle control. End-to-end approaches that use a single multimodal large language model (MLLM) to map language directly to actions struggle with this mismatch, often failing to exploit the reasoning capabilities of the model and exhibiting limited generalization beyond the distributions of driving datasets used for fine-tuning.