# LangCoop: Collaborative Driving with Language

arXiv: https://arxiv.org/abs/2504.13406 · HTML: https://mlsanity.com/paper/2504.13406/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 52 of its 68 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 30 of 52)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving](https://mlsanity.com/paper/2312.09245/) (2023)
- [EMMA: End-to-End Multimodal Model for Autonomous Driving](https://mlsanity.com/paper/2410.23262/) (2024)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning](https://mlsanity.com/paper/2503.07608/) (2025)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2412.15208/) (2024)
- [An Extensible Framework for Open Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2401.13964/) (2024)
- [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://mlsanity.com/paper/2303.13560/) (2023)
- [DSDNet: Deep Structured self-Driving Network](https://mlsanity.com/paper/2008.06041/) (2020)
- [Bridging the Domain Gap for Multi-Agent Perception](https://mlsanity.com/paper/2210.08451/) (2022)
- [Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models](https://mlsanity.com/paper/2310.17642/) (2023)
- [V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models](https://mlsanity.com/paper/2408.09251/) (2024)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [CORE: Cooperative Reconstruction for Multi-Agent Perception](https://mlsanity.com/paper/2307.11514/) (2023)
- [Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving](https://mlsanity.com/paper/2405.15324/) (2024)
- [On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective](https://mlsanity.com/paper/2502.14296/) (2025)
- [AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning](https://mlsanity.com/paper/2404.06345/) (2024)
- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications](https://mlsanity.com/paper/2401.00124/) (2023)

## Built on by (most cited first, 25 of 25)

- [DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving](https://mlsanity.com/paper/2510.12796/) (2025)
- [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025)
- [Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future](https://mlsanity.com/paper/2512.16760/) (2025)
- [V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving](https://mlsanity.com/paper/2506.02580/) (2025)
- [DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving](https://mlsanity.com/paper/2605.28544/) (2026)
- [Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2505.20223/) (2025)
- [AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration](https://mlsanity.com/paper/2506.19283/) (2025)
- [QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception](https://mlsanity.com/paper/2509.03704/) (2025)
- [SteerVLA: Steering Vision-Language-Action Models in Long-Tail Driving Scenarios](https://mlsanity.com/paper/2602.08440/) (2026)
- [Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition](https://mlsanity.com/paper/2507.21610/) (2025)
- [Generative AI for Autonomous Driving: Frontiers and Opportunities](https://mlsanity.com/paper/2505.08854/) (2025)
- [Automated Vehicles Should be Connected with Natural Language](https://mlsanity.com/paper/2507.01059/) (2025)
- [SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving](https://mlsanity.com/paper/2510.18123/) (2025)
- [OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving](https://mlsanity.com/paper/2606.21165/) (2026)
- [UNCAP: Uncertainty-Guided Neurosymbolic Planning Using Natural Language Communication for Cooperative Autonomous Vehicles](https://mlsanity.com/paper/2510.12992/) (2025)
- [Background Fades, Foreground Leads: Curriculum-Guided Background Pruning for Efficient Foreground-Centric Collaborative Perception](https://mlsanity.com/paper/2510.19250/) (2025)
- [OpenCOOD-Air: Prompting Heterogeneous Ground-Air Collaborative Perception with Spatial Conversion and Offset Prediction](https://mlsanity.com/paper/2603.13919/) (2026)
- [Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs](https://mlsanity.com/paper/2606.23938/) (2026)
- [Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation](https://mlsanity.com/paper/2509.05751/) (2025)
- [Safety Case Patterns for VLA-based driving systems: Insights from SimLingo](https://mlsanity.com/paper/2603.16013/) (2026)
- [LACO: Adaptive Latent Communication for Collaborative Driving](https://mlsanity.com/paper/2605.22504/) (2026)
- [Multi-Agent Embodied Autonomous Driving (MAEAD): From V2X Information Exchange to Shared World Models](https://mlsanity.com/paper/2606.13840/) (2026)
- [G-MARK: Grounded Multi-Agent Reasoning for Cooperative Driving via Knowledge Graphs](https://mlsanity.com/paper/2608.19964/) (2026)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)
- [CoLMIN: LLM-based Multi-Decision Path Negotiation for Cooperative Autonomous Driving](https://mlsanity.com/paper/2609.04807/) (2026)

## Abstract (arXiv)

Multi-agent collaboration holds great promise for enhancing the safety, reliability, and mobility of autonomous driving systems by enabling information sharing among multiple connected agents. However, existing multi-agent communication approaches are hindered by limitations of existing communication media, including high bandwidth demands, agent heterogeneity, and information loss. To address these challenges, we introduce LangCoop, a new paradigm for collaborative autonomous driving that leverages natural language as a compact yet expressive medium for inter-agent communication. LangCoop features two key innovations: Mixture Model Modular Chain-of-thought (M3CoT) for structured zero-shot vision-language reasoning and Natural Language Information Packaging (LangPack) for efficiently packaging information into concise, language-based messages. Through extensive experiments conducted in the CARLA simulations, we demonstrate that LangCoop achieves a remarkable 96% reduction in communication bandwidth (< 2KB per message) compared to image-based communication, while maintaining competitive driving performance in the closed-loop evaluation. Our project page and code are at https://xiangbogaobarry.github.io/LangCoop/.