Papers by Xun Gong

2 papers
From Language to Driving: A Dual-Loop SLM-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language (2026.acl-long)

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Challenge: Recent large language model-based AD research offers new avenues to address this challenge.
Approach: They propose a small language model (SLM) for high-level semantic reasoning and schedule generation, while an inner loop performs low-level, high-frequency schedule execution and vehicle control.
Outcome: The proposed framework improves instruction completion rates while maintaining high safety and compliance relative to multiple baselines.
Harnessing and Evaluating the Intrinsic Extrapolation Ability of Large Language Models for Vehicle Trajectory Prediction (2025.naacl-long)

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Challenge: Emergent abilities of large language models (LLMs) have advanced their application in autonomous vehicle research.
Approach: They propose a framework that leverages LLMs’ built-in extrapolation capabilities for vehicle trajectory prediction, enabling them to understand traffic agents' behavior and interactions over time.
Outcome: The proposed framework enables off-the-shelf, frozen LLMs to achieve competitive trajectory prediction performance with qualitative analyses revealing their enhanced understanding of complex, multi-agent traffic scenarios.

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