Papers by Can Cui
Learning Autonomous Driving Tasks via Human Feedbacks with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing systems focus on making autonomous driving decisions without human interaction, but human-like decision-making is still an important factor in designing autonomous driving systems. |
| Approach: | They propose a framework leveraging Large Language Models for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback. |
| Outcome: | The proposed framework can match baseline extensively trained reinforcement learning models in driving scenarios and store optimal driving programming policy using Retrieval Augmented Generation (RAG). |
DASR: Distributed Adaptive Scene Recognition - A Multi-Agent Cloud-Edge Framework for Language-Guided Scene Detection (2025.emnlp-industry)
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| Challenge: | Current approaches to analyzing driving scenarios rely on massive data collection followed by manual filtering. |
| Approach: | They propose a cloud-based framework for language-guided scene detection in connected vehicles . the framework leverages cloud- and edge-deployed large language models to identify relevant driving scenarios while optimizing on-vehicle buffer storage. |
| Outcome: | The proposed framework performs better on complex driving tasks and reduces storage requirements. |