An LLM-supported framework maps natural-language commands to distinguishable Apollo lane-change parameters for three driving styles via clustering and RAG, with experiments showing improved interpretation of implicit preferences.
Vision language models in autonomous driving: A survey and outlook
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C-CoT applies VLMs to autonomous driving via five-stage reasoning with a meta-action tree for counterfactuals, yielding 81.9% risk recall, 3.52% collision rate, and 1.98 m L2 error on a new dataset.
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A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios
An LLM-supported framework maps natural-language commands to distinguishable Apollo lane-change parameters for three driving styles via clustering and RAG, with experiments showing improved interpretation of implicit preferences.
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C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving
C-CoT applies VLMs to autonomous driving via five-stage reasoning with a meta-action tree for counterfactuals, yielding 81.9% risk recall, 3.52% collision rate, and 1.98 m L2 error on a new dataset.