Automated Vehicles Should be Connected with Natural Language
This addresses communication bottlenecks for automated vehicles in multi-agent systems, though it appears incremental as it builds on existing collaborative driving frameworks.
The paper tackles limitations in multi-agent collaborative driving communication by proposing a transition from perception-oriented data exchanges to natural language communication of intent and reasoning, which promises to advance safety, efficiency, and transparency in intelligent transportation systems.
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.