Automated Vehicles Should be Connected with Natural Language
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913921991966720 |
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| author | Gao, Xiangbo Wu, Keshu Zhang, Hao Tian, Kexin Zhou, Yang Tu, Zhengzhong |
| author_facet | Gao, Xiangbo Wu, Keshu Zhang, Hao Tian, Kexin Zhou, Yang Tu, Zhengzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Vehicles Should be Connected with Natural Language Gao, Xiangbo Wu, Keshu Zhang, Hao Tian, Kexin Zhou, Yang Tu, Zhengzhong Multiagent Systems Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Robotics 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. |
| title | Automated Vehicles Should be Connected with Natural Language |
| topic | Multiagent Systems Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2507.01059 |