A retrieval-augmented LLM framework that lets a driving model query a database of environmental sensor data reduces reported trajectory prediction error by roughly 70 percent, but the evaluation design inflates the gain.
Review the state-of-the-art technologies of seman- tic segmentation based on deep learning
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SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving
A retrieval-augmented LLM framework that lets a driving model query a database of environmental sensor data reduces reported trajectory prediction error by roughly 70 percent, but the evaluation design inflates the gain.