TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909955876978688 |
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| author | Wang, Maonan Chen, Yirong Cai, Yuxin Pang, Aoyu Xie, Yuejiao Ma, Zian Xu, Chengcheng Jiang, Kemou Wang, Ding Roullet, Laurent Chen, Chung Shue Cui, Zhiyong Kan, Yuheng Lepech, Michael Pun, Man-On |
| author_facet | Wang, Maonan Chen, Yirong Cai, Yuxin Pang, Aoyu Xie, Yuejiao Ma, Zian Xu, Chengcheng Jiang, Kemou Wang, Ding Roullet, Laurent Chen, Chung Shue Cui, Zhiyong Kan, Yuheng Lepech, Michael Pun, Man-On |
| contents | Air-ground collaborative intelligence is becoming a key approach for next-generation urban intelligent transportation management, where aerial and ground systems work together on perception, communication, and decision-making. However, the lack of a unified multi-modal simulation environment has limited progress in studying cross-domain perception, coordination under communication constraints, and joint decision optimization. To address this gap, we present TranSimHub, a unified simulation platform for air-ground collaborative intelligence. TranSimHub offers synchronized multi-view rendering across RGB, depth, and semantic segmentation modalities, ensuring consistent perception between aerial and ground viewpoints. It also supports information exchange between the two domains and includes a causal scene editor that enables controllable scenario creation and counterfactual analysis under diverse conditions such as different weather, emergency events, and dynamic obstacles. We release TranSimHub as an open-source platform that supports end-to-end research on perception, fusion, and control across realistic air and ground traffic scenes. Our code is available at https://github.com/Traffic-Alpha/TransSimHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making Wang, Maonan Chen, Yirong Cai, Yuxin Pang, Aoyu Xie, Yuejiao Ma, Zian Xu, Chengcheng Jiang, Kemou Wang, Ding Roullet, Laurent Chen, Chung Shue Cui, Zhiyong Kan, Yuheng Lepech, Michael Pun, Man-On Systems and Control Machine Learning Multiagent Systems Air-ground collaborative intelligence is becoming a key approach for next-generation urban intelligent transportation management, where aerial and ground systems work together on perception, communication, and decision-making. However, the lack of a unified multi-modal simulation environment has limited progress in studying cross-domain perception, coordination under communication constraints, and joint decision optimization. To address this gap, we present TranSimHub, a unified simulation platform for air-ground collaborative intelligence. TranSimHub offers synchronized multi-view rendering across RGB, depth, and semantic segmentation modalities, ensuring consistent perception between aerial and ground viewpoints. It also supports information exchange between the two domains and includes a causal scene editor that enables controllable scenario creation and counterfactual analysis under diverse conditions such as different weather, emergency events, and dynamic obstacles. We release TranSimHub as an open-source platform that supports end-to-end research on perception, fusion, and control across realistic air and ground traffic scenes. Our code is available at https://github.com/Traffic-Alpha/TransSimHub. |
| title | TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making |
| topic | Systems and Control Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2510.15365 |