打造空地协同仿真平台，助力智能交通感知与决策研究
TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making
- 构建空地同步多模态渲染系统，支持视觉、深度、语义一致性感知
- 实现空地信息交互与可控场景生成，支持多种天气和突发事件模拟
- 开源平台支持端到端感知融合与控制研究，适合交通智能化开发者
空地协同智能是下一代城市智能交通管理的关键方向，空中与地面系统在感知、通信和决策上协同工作。然而，缺乏统一的多模态仿真环境限制了跨域感知、通信受限下的协同以及联合决策优化的研究进展。为此，我们提出 TranSimHub，一个面向空地协同智能的统一仿真平台。TranSimHub 提供 RGB、深度和语义分割多模态的同步多视角渲染，确保空中与地面视角间感知一致性。平台支持跨域信息交换，并集成因果场景编辑器，可生成可控场景并进行不同条件下的反事实分析，如不同天气、紧急事件和动态障碍物等。我们开源发布 TranSimHub，支持在真实空地交通场景中开展感知、融合与控制的端到端研究。代码已公开于 https://github.com/Traffic-Alpha/TransSimHub。
原文摘要 · Abstract (English)
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.
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