TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making

2025-10-17

AI Summary

Current research lacks a unified multimodal simulation environment supporting cross-domain perception, communication-constrained coordination, and joint decision optimization—hindering the advancement of air-ground collaborative intelligence in urban traffic management. To address this, we propose TranSimHub: the first open-source simulation platform specifically designed for air-ground collaborative intelligence. It integrates synchronized RGB/depth/semantic segmentation rendering, a synchronous multi-view causal scenario editor, and an air-ground information interaction mechanism. The platform enables high-fidelity modeling and counterfactual analysis under controllable conditions—including weather disturbances and emergency events. Crucially, TranSimHub unifies cross-domain perception, communication, and decision-making simulation within realistic urban traffic scenarios. It provides a reproducible benchmark for end-to-end perception fusion and collaborative control research, advancing both methodological rigor and practical applicability in intelligent transportation systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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/TranSimHub.

Problem

• Lacks unified simulation for air-ground multimodal perception
• Addresses coordination challenges under communication constraints
• Enables joint decision optimization across aerial-ground systems

Innovation

• Unified simulation platform for air-ground collaboration
• Synchronized multi-view rendering across RGB and depth
• Causal scene editor for controllable scenario creation