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.
Maveric: A data-driven approach to personalized autonomous driving,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Develops a bidirectional trust framework for human-led truck platooning with distinct human-to-automation and automation-to-human dimensions, a quantitative model centered on following distance, and derived design guidelines.
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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Human-Machine Bidirectional Trust-Aware Analysis and Design for Human-Led Truck Platooning
Develops a bidirectional trust framework for human-led truck platooning with distinct human-to-automation and automation-to-human dimensions, a quantitative model centered on following distance, and derived design guidelines.