@inproceedings{liu-etal-2026-language,
    title = "From Language to Driving: A Dual-Loop {SLM}-Enhanced Framework for Multi-Planner Scheduling via a Domain-Specific Language",
    author = "Liu, Jiawei  and
      Gong, Xun  and
      Yang, Muli  and
      Yu, Xingrui  and
      Fang, Fen  and
      Yang, Xulei  and
      Tsang, Ivor  and
      hu, Yunfeng  and
      Chen, Hong  and
      Guo, Qing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1381/",
    pages = "29957--29973",
    ISBN = "979-8-89176-390-6",
    abstract = "Advancing from usable to collaborative autonomy requires driving systems to execute passenger instructions safely and reliably. This work formulates instruction realization as scheduling across multiple motion planners and presents a dual-loop framework that provides a transparent decision chain from natural language to vehicle control. The outer loop uses a small language model (SLM) for high-level, low-frequency semantic reasoning and schedule generation, while the inner loop performs low-level, high-frequency schedule execution and vehicle control. To compensate for the SLM{'}s limited capacity, the framework integrates receding-horizon scheduling to segment long-horizon instruction tasks, a domain-specific language (DSL) that restricts SLM outputs to a scheduling-oriented subspace, and reinforcement learning in high-fidelity urban traffic to refine the SLM{'}s DSL proficiency and scheduling performance. Experiments show that the framework improves instruction-completion rates while maintaining high safety and compliance relative to multiple baselines."
}