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

arXiv: https://arxiv.org/abs/2510.15365 · HTML: https://mlsanity.com/paper/2510.15365/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology](https://mlsanity.com/paper/2410.07087/) (2024)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Openfly: A comprehensive platform for aerial vision-language navigation](https://mlsanity.com/paper/2502.18041/) (2025)
- [LibSignal: An Open Library for Traffic Signal Control](https://mlsanity.com/paper/2211.10649/) (2022)
- [CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models](https://mlsanity.com/paper/2402.06127/) (2024)

## Built on by (most cited first, 3 of 3)

- [CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence](https://mlsanity.com/paper/2603.28032/) (2026)
- [ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control](https://mlsanity.com/paper/2605.29425/) (2026)
- [VLALight: A Vision-Language-Action Model for Traffic Signal Control](https://mlsanity.com/paper/2609.36934/) (2026)

## Abstract (arXiv)

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.