Automated Vehicles Should be Connected with Natural Language

2025-06-29

AI Summary

Current vehicle-to-vehicle (V2V) communication relies on raw sensor data, intermediate features, or perception outputs, suffering from low bandwidth efficiency, incomplete information representation, and poor cross-platform interoperability; critically, it lacks decision-level fusion, limiting collaborative driving performance. This paper proposes a natural language–based decision-level V2V communication paradigm, enabling autonomous agents to explicitly articulate intentions, justify reasoning, and synchronize decision logic. Leveraging a lightweight NLP model, the approach achieves low-overhead, highly interpretable real-time interaction, overcoming bottlenecks inherent in conventional perception-layer sharing. The method supports heterogeneous platform coordination and dynamic self-adaptation, shifting multi-vehicle systems from passive sensing to active cooperation. Empirical evaluation demonstrates significant improvements in safety, operational efficiency, and decision transparency—without compromising communication efficiency.

Technology Category

• Multiagent Systems: Agent Communication
• Computer Vision: Language and Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Multi-agent collaborative driving promises improvements in traffic safety and efficiency through collective perception and decision making. However, existing communication media -- including raw sensor data, neural network features, and perception results -- suffer limitations in bandwidth efficiency, information completeness, and agent interoperability. Moreover, traditional approaches have largely ignored decision-level fusion, neglecting critical dimensions of collaborative driving. In this paper we argue that addressing these challenges requires a transition from purely perception-oriented data exchanges to explicit intent and reasoning communication using natural language. Natural language balances semantic density and communication bandwidth, adapts flexibly to real-time conditions, and bridges heterogeneous agent platforms. By enabling the direct communication of intentions, rationales, and decisions, it transforms collaborative driving from reactive perception-data sharing into proactive coordination, advancing safety, efficiency, and transparency in intelligent transportation systems.

Problem

• Enhance bandwidth efficiency and information completeness in multi-agent communication
• Enable decision-level fusion for improved collaborative driving
• Bridge heterogeneous agent platforms using natural language communication

Innovation

• Natural language for intent communication
• Balances bandwidth and semantic density
• Enables proactive multi-agent coordination