Automated Vehicles Should be Connected with Natural Language

TL;DR

This paper advocates for using natural language as a communication medium among automated vehicles to improve collaboration, safety, and efficiency by enabling explicit intent sharing and reasoning.

Contribution

It introduces a novel approach of replacing traditional perception data exchange with natural language communication for multi-agent collaborative driving.

Findings

Natural language provides a semantically rich, bandwidth-efficient communication method.

Language-based communication enhances interoperability among heterogeneous vehicle agents.

Proactive intent sharing via language improves traffic safety and coordination.

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.