Off-topic for aiqbench
This story was gathered from a source we follow but falls outside AI and quantum coverage, so there is no aiqbench take for it.
A Corridor-Scale CARLA-VISSIM Co-Simulation Framework for Multi-Intersection Urban Traffic
arXiv:2606.15431v1 Announce Type: new Abstract: 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.
Abstract: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.
Robotics (cs.RO)
arXiv:2606.15431 [cs.RO]
(or arXiv:2606.15431v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.15431 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 276 words

