UNCAP: Uncertainty-Guided Planning Using Natural Language Communication for Cooperative Autonomous Vehicles

• URL: http://arxiv.org/abs/2510.12992v1
• Date: Tue, 14 Oct 2025 21:09:09 GMT
• Title: UNCAP: Uncertainty-Guided Planning Using Natural Language Communication for Cooperative Autonomous Vehicles
• Authors: Neel P. Bhatt, Po-han Li, Kushagra Gupta, Rohan Siva, Daniel Milan, Alexander T. Hogue, Sandeep P. Chinchali, David Fridovich-Keil, Zhangyang Wang, Ufuk Topcu,
• Abstract summary: Uncertainty-Guided Natural Language Cooperative Autonomous Planning (UNCAP) is a vision-language model-based planning approach.<n>It enables CAVs to communicate via lightweight natural language messages while explicitly accounting for perception uncertainty in decision-making.<n> Experiments across diverse driving scenarios show a 63% reduction in communication bandwidth with a 31% increase in driving safety score, a 61% reduction in decision uncertainty, and a four-fold increase in collision distance margin.
• Score: 79.10221881250759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe large-scale coordination of multiple cooperative connected autonomous vehicles (CAVs) hinges on communication that is both efficient and interpretable. Existing approaches either rely on transmitting high-bandwidth raw sensor data streams or neglect perception and planning uncertainties inherent in shared data, resulting in systems that are neither scalable nor safe. To address these limitations, we propose Uncertainty-Guided Natural Language Cooperative Autonomous Planning (UNCAP), a vision-language model-based planning approach that enables CAVs to communicate via lightweight natural language messages while explicitly accounting for perception uncertainty in decision-making. UNCAP features a two-stage communication protocol: (i) an ego CAV first identifies the subset of vehicles most relevant for information exchange, and (ii) the selected CAVs then transmit messages that quantitatively express their perception uncertainty. By selectively fusing messages that maximize mutual information, this strategy allows the ego vehicle to integrate only the most relevant signals into its decision-making, improving both the scalability and reliability of cooperative planning. Experiments across diverse driving scenarios show a 63% reduction in communication bandwidth with a 31% increase in driving safety score, a 61% reduction in decision uncertainty, and a four-fold increase in collision distance margin during near-miss events. Project website: https://uncap-project.github.io/

Related papers

• Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning [60.650628083185616]
  We propose a three-stage communication-aware distributed learning framework to improve training and inference efficiency.<n>In StageI, devices perform local multi-modal self-supervised learning to obtain shared and modality-specific encoders without device--server exchange.<n>StageII, distributed fine-tuning with centralized evidential fusion calibrates per-modality uncertainty and reliably aggregates features distorted by noise or channel fading.<n>StageIII, an uncertainty-guided feedback mechanism selectively requests additional features for uncertain samples, optimizing the communication--accuracy tradeoff in the distributed setting.
  arXiv  Detail & Related papers  (2026-01-21T12:38:02Z)
• SRA-CP: Spontaneous Risk-Aware Selective Cooperative Perception [21.17534913288233]
  This paper proposes a Spontaneous Risk-Aware Selective Cooperative Perception (SRA-CP) framework to address these challenges.<n>SRA-CP achieves less than 1% average precision (AP) loss for safety-critical objects compared to generic CP.<n>It improves the perception performance by 15% over existing selective CP methods that do not incorporate risk awareness.
  arXiv  Detail & Related papers  (2025-11-21T18:03:48Z)
• Automated Vehicles Should be Connected with Natural Language [10.579888130257185]
  Multi-agent collaborative driving promises improvements in traffic safety and efficiency through collective perception and decision making.<n>Existing communication media suffer limitations in bandwidth efficiency, information completeness, and agent interoperability.<n>We argue that addressing these challenges requires a transition from purely perception-oriented data exchanges to explicit intent and reasoning communication using natural language.
  arXiv  Detail & Related papers  (2025-06-29T16:41:19Z)
• Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks [55.32199894495722]
  We investigate an LMM-based vehicle AI assistant using a Large Language and Vision Assistant (LLaVA)<n>To reduce computational demands and shorten response time, we optimize LLaVA's image slicing to selectively focus on areas of utmost interest to users.<n>We construct a Visual Question Answering (VQA) dataset for traffic scenarios to evaluate effectiveness.
  arXiv  Detail & Related papers  (2025-05-05T07:18:47Z)
• LangCoop: Collaborative Driving with Language [13.25814019477039]
  LangCoop is a new paradigm for collaborative autonomous driving that leverages natural language as a compact yet expressive medium for inter-agent communication.<n>LangCoop achieves a remarkable 96% reduction in communication bandwidth ( 2KB per message) compared to image-based communication.
  arXiv  Detail & Related papers  (2025-04-18T02:03:14Z)
• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [87.7482313774741]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.<n>This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism [23.824400533836535]
  We introduce SmartCooper, an adaptive collaborative perception framework that incorporates communication optimization and a judger mechanism. Our results demonstrate a substantial reduction in communication costs by 23.10% compared to the non-judger scheme.
  arXiv  Detail & Related papers  (2024-02-01T04:15:39Z)
• Selective Communication for Cooperative Perception in End-to-End Autonomous Driving [8.680676599607123]
  We propose a novel selective communication algorithm for cooperative perception. Our algorithm is shown to produce higher success rates than a random selection approach on previously studied safety-critical driving scenario simulations.
  arXiv  Detail & Related papers  (2023-05-26T18:13:17Z)
• Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving [49.42873226593071]
  We propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP) for V2X communication-aided autonomous driving. We use historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.
  arXiv  Detail & Related papers  (2023-04-24T04:59:13Z)
• A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles [21.132777568170702]
  We design an information-sharing-based reinforcement learning framework for connected autonomous vehicles. We show that our approach can improve the CAV system's efficiency in terms of average velocity and comfort. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.
  arXiv  Detail & Related papers  (2020-03-09T19:15:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.