AI 中文总结
本文针对自动驾驶场景，提出基于一阶逻辑的目标导向语义通信方法，通过选择关键证据传输实现安全决策，在相同通信预算下优于均匀证据选择。
AI 中文摘要
本文研究面向协作环境（如自动驾驶网络）中神经符号决策的一阶逻辑（First-Order Logic, FOL）语义通信问题。每辆联网自动驾驶车辆（Connected Autonomous Vehicle, CAV）将自身部分传感器观测转换为自然语言场景描述及对应的接地FOL证据。在上行链路预算约束下，每辆车的语义编码器会选择对评估交通规则最具信息性的观测，发送给路侧单元（Road Side Unit, RSU）。RSU融合所有接收的证据，评估协作规则，进行逻辑推演以生成车辆特定的安全与路权信息，用于受限的下行链路传输。每辆CAV将接收的推演结果与本地描述结合，使本地大语言模型（Large Language Model, LLM）智能体能够选择高级驾驶动作。本文提出一种基于归纳逻辑概率的随机支持Dirichlet-分类模型的可验证语义通信方法，对Carnap和Hintikka的系统进行现代统计重解释。基于该模型，推导了面向目标的语义信息瓶颈公式，该公式根据证据对任务目标不确定性的降低程度来优先选择传输的证据。使用从《加州驾驶员手册》中提取的152条交通规则，在CARLA中的MDrive模拟器上对该框架进行评估。结果显示，在相同通信预算下，语义证据选择能完成所有场景且无安全隐患，而均匀证据选择会导致碰撞，证明了语义通信的优越性。
英文摘要
We consider First-Order Logic (FOL)-based semantic communication for neuro-symbolic decision-making in collaborative environments such as autonomous driving networks. Each connected autonomous vehicle (CAV) converts its partial sensor observations into a natural-language scene description and corresponding grounded FOL evidence. Under an uplink budget, a semantic encoder at each car selects the observations most informative for evaluating traffic rules and transmit to a Road Side Unit (RSU). The RSU fuses all received evidence, evaluates collaborative rules, performs logical deduction for vehicle-specific safety and right-of-way information for constrained downlink transmission. Each CAV combines the received deductions with its local description, enabling a local LLM agent to select a high-level driving action. We develop a principled, verifiable semantic communication method using a random-support Dirichlet--Categorical model of inductive logical probability, providing a modern statistical reinterpretation of Carnap's and Hintikka's systems. From this model, we derive a goal-oriented semantic information-bottleneck formulation that prioritizes evidence transmission by its reduction of uncertainty over task goals. Using 152 traffic rules extracted from the California Driver Handbook, we evaluate the framework on MDrive simulator in CARLA. Under identical communication budgets, semantic evidence selection completes every scenario without safety hazards, whereas uniform evidence selection produces collisions, showcasing semantic communication's superiority.
