# TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making Source: [TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making]() Collection index: [llms.txt]() Data updated: 2025-12-13T05:01:26.895Z Authors: Maonan Wang, Yirong Chen, Yuxin Cai, Aoyu Pang, Yuejiao Xie, Zian Ma, Chengcheng Xu, Kemou Jiang, Ding Wang, Laurent Roullet, Chung Shue Chen, Zhiyong Cui, Yuheng Kan, Michael Lepech, Man-On Pun Published: 2025-10-17T06:56:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()