Goal-Oriented Logic-based Semantic Communication for Neuro-Symbolic Reasoning with Applications onto Autonomous Driving

2026-08-01

AI Summary

This study addresses the challenge of efficiently transmitting the most safety-critical semantic information under limited communication bandwidth in cooperative autonomous driving scenarios. The authors propose a goal-oriented, verifiable semantic communication framework that translates onboard sensor observations into natural language descriptions and first-order logic (FOL) evidence. Leveraging a probabilistic inductive logic model—grounded in modern statistical interpretations of Carnap’s and Hintikka’s systems—they formulate a semantic information bottleneck to prioritize and selectively upload key evidence. Roadside units aggregate multi-vehicle evidence for neuro-symbolic reasoning and return tailored right-of-way and safety advisories to vehicles, guiding large language model agents in generating high-level driving actions. Evaluated in CARLA-MDrive using 152 rules from the California Driver Handbook, the method achieves zero accidents under identical communication budgets, significantly outperforming uniform evidence selection strategies.

Abstract

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.

Problem

• semantic communication
• neuro-symbolic reasoning
• autonomous driving
• goal-oriented
• first-order logic

Innovation

• Goal-Oriented Semantic Communication
• Neuro-Symbolic Reasoning
• First-Order Logic
• Inductive Logical Probability
• Semantic Information Bottleneck