This paper presents an implemented CARLA-VISSIM co-simulation framework for an urban corridor comprising approximately fifteen connected intersections centered on Martin Luther King Jr. Boulevard in Chattanooga, Tennessee. The system integrates CARLA 0.10.0 Unreal Engine 5 with PTV VISSIM 2026 through a bidirectional, step-synchronized interface that couples VISSIM's microscopic vehicle, pedestrian, and signal-controller logic with CARLA's high-fidelity 3D rendering. A LiDAR-derived elevation model and RoadRunner-based High Definition (HD) map provide terrain-accurate road geometry deployed consistently across both simulators. The framework incorporates explicit actor ownership, mirrored lifecycle management, coordinate reconciliation, and a latest-state-per-actor update policy, enabling stable interaction between VISSIM-controlled traffic and a CARLA-controlled ego vehicle. A corridor-scale case study demonstrates consistent traffic-signal mirroring, synchronized vehicle-pedestrian interactions, and stable mixed-authority operation under peak loads of approximately 100 vehicles and 100 pedestrians. The deployment captures the interaction of the five signalized intersections along MLK Street and their connecting upstream and downstream intersections, revealing synchronization challenges unique to multi-intersection corridors. Results indicate that this MLK-centered corridor provides an effective testbed for verifying cross-simulator consistency and that the proposed architecture supports reliable, perception-ready co-simulation for corridor-level traffic studies.

翻译：本文提出一种针对城市走廊的CARLA-VISSIM协同仿真框架，该走廊以田纳西州查塔努加市马丁·路德·金大道为中心，包含约15个互联交叉口。系统通过双向步进同步接口整合CARLA 0.10.0虚幻引擎5与PTV VISSIM 2026，将VISSIM的微观车辆、行人及信号控制逻辑与CARLA的高保真三维渲染相耦合。基于激光雷达提取的高程模型和RoadRunner生成的高清地图，为两个仿真器提供地形精确的道路几何坐标，并在两者间保持一致部署。该框架明确了参与者所有权、镜像生命周期管理、坐标校正及按参与者更新最新状态的策略，实现了VSSIM控制下的交通流与CARLA控制的自车之间的稳定交互。走廊尺度案例研究表明：在约100辆车和100名行人的峰值负载下，框架实现了交通信号镜像、车-人同步交互及稳定的混合权限运行。该部署捕获了MLK街道沿线五个信号化交叉口及其上下游连接交叉口的交互行为，揭示了多交叉口走廊特有的同步挑战。结果表明，这一以MLK大道为中心的走廊为验证跨仿真器一致性提供了有效测试平台，且所提出的架构可支撑面向走廊级交通研究的可靠且支持感知的协同仿真。