title: Interact, Instruct to Improve: A LLM-Driven Parallel Actor-Reasoner
    Framework for Enhancing Autonomous Vehicle Interactions
summary: Autonomous Vehicles (AVs) have entered the commercialization stage, but their
  limited ability to interact and express intentions still poses challenges in
  interactions with Human-driven Vehicles (HVs). Recent advances in large
  language models (LLMs) enable bidirectional human-machine communication, but
  the conflict between slow inference speed and the need for real-time
  decision-making challenges practical deployment. To address these issues, this
  paper introduces a parallel Actor-Reasoner framework designed to enable
  explicit bidirectional AV-HV interactions across multiple scenarios. First, by
  facilitating interactions between the LLM-driven Reasoner and heterogeneous
  simulated HVs during training, an interaction memory database, referred to as
  the Actor, is established. Then, by introducing the memory partition module and
  the two-layer memory retrieval module, the Actor's ability to handle
  heterogeneous HVs is significantly enhanced. Ablation studies and comparisons
  with other decision-making methods demonstrate that the proposed Actor-Reasoner
  framework significantly improves safety and efficiency. Finally, with the
  combination of the external Human-Machine Interface (eHMI) information derived
  from Reasoner's reasoning and the feasible action solutions retrieved from the
  Actor, the effectiveness of the proposed Actor-Reasoner is confirmed in
  multi-scenario field interactions. Our code is available at
  https://github.com/FanGShiYuu/Actor-Reasoner.
paper_authors._id: 67c8427047c2aa135346dced
paper_authors.name: Shiyu Fang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-05T15:46:48.691Z
paper_authors._id: 66d3290364c1e9b73208af82
paper_authors.avatarUrl: /avatars/e0ea5f2b366927c7b146f248028a2e59.svg
paper_authors.fullname: Shiyu Fang
paper_authors.type: user
paper_authors.user: FanGShiYuu
paper_authors._id: 67c8427047c2aa135346dcee
paper_authors.name: Jiaqi Liu
paper_authors._id: 67c8427047c2aa135346dcef
paper_authors.name: Chengkai Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T13:57:59.218Z
paper_authors._id: 6319e98c02fb3220373762f6
paper_authors.avatarUrl: /avatars/1456665d2ae6a284e2505744fec0cb1d.svg
paper_authors.fullname: Chengkai Xu
paper_authors.type: user
paper_authors.user: Shinghoi
paper_authors._id: 67c8427047c2aa135346dcf0
paper_authors.name: Chen Lv
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T13:57:52.421Z
paper_authors._id: 670bcbbcdb7d982eb6a7e027
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2aSOLBxM_MgGtK0EFJAKL.png
paper_authors.fullname: Chen Lv
paper_authors.type: user
paper_authors.user: zafkiellc
paper_authors._id: 67c8427047c2aa135346dcf1
paper_authors.name: Peng Hang
paper_authors._id: 67c8427047c2aa135346dcf2
paper_authors.name: Jian Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T14:06:29.074Z
paper_authors._id: 6522245b54967a3a49b3b22b
paper_authors.avatarUrl: /avatars/18d190808f91eaff4b81cec1e9690337.svg
paper_authors.fullname: Jian SUN
paper_authors.type: user
paper_authors.user: jiansun-china
paper_title: Interact, Instruct to Improve: A LLM-Driven Parallel Actor-Reasoner
    Framework for Enhancing Autonomous Vehicle Interactions
paper_summary: Autonomous Vehicles (AVs) have entered the commercialization stage, but their
  limited ability to interact and express intentions still poses challenges in
  interactions with Human-driven Vehicles (HVs). Recent advances in large
  language models (LLMs) enable bidirectional human-machine communication, but
  the conflict between slow inference speed and the need for real-time
  decision-making challenges practical deployment. To address these issues, this
  paper introduces a parallel Actor-Reasoner framework designed to enable
  explicit bidirectional AV-HV interactions across multiple scenarios. First, by
  facilitating interactions between the LLM-driven Reasoner and heterogeneous
  simulated HVs during training, an interaction memory database, referred to as
  the Actor, is established. Then, by introducing the memory partition module and
  the two-layer memory retrieval module, the Actor's ability to handle
  heterogeneous HVs is significantly enhanced. Ablation studies and comparisons
  with other decision-making methods demonstrate that the proposed Actor-Reasoner
  framework significantly improves safety and efficiency. Finally, with the
  combination of the external Human-Machine Interface (eHMI) information derived
  from Reasoner's reasoning and the feasible action solutions retrieved from the
  Actor, the effectiveness of the proposed Actor-Reasoner is confirmed in
  multi-scenario field interactions. Our code is available at
  https://github.com/FanGShiYuu/Actor-Reasoner.
paper_ai_keywords: Actor-Reasoner framework large language models (LLMs) bidirectional human-machine communication interaction memory database memory partition module two-layer memory retrieval module external Human-Machine Interface (eHMI) reasoning feasible action solutions multi-scenario field interactions