用主动推理模型模拟道路用户如何化解空间冲突，揭示规则与沟通的双刃剑效应。
Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model
- 基于主动推理构建双智能体交互模型，融合行为耦合、规范预期和显式沟通。
- 规则与沟通能提升冲突解决成功率，但依赖对方按预期行动。
- 当对方违规或误导时，过度依赖规则反而易引发碰撞，适用于自动驾驶设计。
理解道路使用者如何解决空间共享冲突对交通安全及自动驾驶部署至关重要。现有模型虽捕捉了特定交互特征（如显式沟通），但缺乏理论基础的计算框架。本文将先前开发的主动推理驱动行为模型扩展至双智能体交互场景，整合三种不确定性降低机制：(i) 通过直接行为耦合实现隐式沟通，(ii) 依赖规范性预期（如停车标志、优先权规则），(iii) 显式沟通。在简化交叉口场景中，我们发现规范性与显式沟通线索可提高成功冲突化解概率，但前提是双方均按预期行为。当一方（有意或无意）违反规范或传递误导信息时，依赖这些线索反而可能诱发碰撞。研究表明，主动推理为道路使用者交互建模提供了新范式，且具跨领域适用性。
原文摘要 · Abstract (English)
Understanding how road users resolve space-sharing conflicts is important both for traffic safety and the safe deployment of autonomous vehicles. While existing models have captured specific aspects of such interactions (e.g., explicit communication), a theoretically-grounded computational framework has been lacking. In this paper, we extend a previously developed active inference-based driver behavior model to simulate interactive behavior of two agents. Our model captures three complementary mechanisms for uncertainty reduction in interaction: (i) implicit communication via direct behavioral coupling, (ii) reliance on normative expectations (stop signs, priority rules, etc.), and (iii) explicit communication. In a simplified intersection scenario, we show that normative and explicit communication cues can increase the likelihood of a successful conflict resolution. However, this relies on agents acting as expected. In situations where another agent (intentionally or unintentionally) violates normative expectations or communicates misleading information, reliance on these cues may induce collisions. These findings illustrate how active inference can provide a novel framework for modeling road user interactions which is also applicable in other fields.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。