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.
Personalized autonomous driving with large language models: Field experiments,
2 Pith papers cite this work. Polarity classification is still indexing.
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RIA is a closed-loop system where an LLM reasons about actions, a world model imagines outcomes via rollouts, and a safety scorer selects the best action, demonstrated in CARLA with 80.05% route completion and low collision rate.
citing papers explorer
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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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Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving
RIA is a closed-loop system where an LLM reasons about actions, a world model imagines outcomes via rollouts, and a safety scorer selects the best action, demonstrated in CARLA with 80.05% route completion and low collision rate.