发表机构
Institute of Energy Efficient Mobility; Karlsruhe University of Applied Sciences（高效出行研究所; 卡尔斯鲁厄应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CARLAverse，一个开源多模态仿真框架，通过分布式物理架构将驾驶、骑行和行人仿真集成，实现低延迟触觉反馈的跨机构人机协同实验。
AI 中文摘要
自动驾驶的发展要求在涉及弱势道路使用者（VRU）的混合交通场景中进行全面测试，而纯人工智能体往往无法捕捉真实的人类社会协商行为。虽然人机协同（HITL）仿真器能够安全地研究这些交互，但现有的多智能体平台难以满足高保真触觉反馈所需的网络延迟和同步约束。为解决这一问题，我们提出了CARLAverse，一个开源的多模态仿真生态系统。通过扩展模块化硬件抽象，CARLAverse将驾驶（DrivoCARLA）、骑行（CycloCARLA）和行人（WalkoCARLA）仿真器集成到一个共享的虚拟环境中。其核心方法论贡献在于分布式物理架构：对延迟敏感的自我车辆动力学和高频力反馈在客户端节点上本地计算，而中央CARLA服务器负责非玩家角色（NPC）的物理和全局交通流。通过将触觉控制回路与网络瓶颈解耦，CARLAverse能够在不牺牲物理沉浸感的前提下，实现可扩展的、跨机构的HITL实验。代码和文档：此https URL。
英文摘要
The development of autonomous driving demands comprehensive testing in mixed-traffic scenarios involving vulnerable road users (VRUs), where purely artificial agents often fail to capture authentic human social negotiations. While human-in-the-loop (HITL) simulators enable safe investigation of these interactions, existing multi-agent platforms struggle with the network latency and synchronization constraints required for high-fidelity haptic feedback. To resolve this, we present CARLAverse, an open-source, multimodal simulation ecosystem. Extending modular hardware abstraction, CARLAverse integrates driving (DrivoCARLA), cycling (CycloCARLA), and pedestrian (WalkoCARLA) simulators into a shared virtual environment. Its core methodological contribution is a distributed physics architecture: latency-critical ego dynamics and high-frequency force feedback are computed locally on client nodes, while a central CARLA server orchestrates non-player character (NPC) physics and global traffic. By decoupling haptic control loops from network bottlenecks, CARLAverse enables scalable, cross-institutional HITL experiments without compromising physical immersion. Code and documentation: https://git.ieem-ka.de/simulator-environments/carlaverse
Comments19 pages, 13 figures, Code available at https://git.ieem-ka.de/simulator-environments/carlaverse
