Mohamed El-Darieby

Zubair Islam, Mohamed El-Darieby

Simulating and validating coordination among multiple autonomous vehicles remains challenging, as many existing simulation architectures are limited to single-vehicle operation or rely on centralized control. This paper presents the Distributed Multi-Autonomous Vehicle Architecture (DMAVA), a simulation architecture that enables concurrent execution of multiple independent vehicle autonomy stacks distributed across multiple physical hosts within a shared simulation environment. Each vehicle operates its own complete autonomous driving stack while maintaining coordinated behavior through a data-centric communication layer. The proposed system integrates ROS 2 Humble, Autoware Universe, AWSIM Labs, and Zenoh to support high data accuracy and controllability during multi-vehicle simulation, enabling consistent perception, planning, and control behavior under distributed execution. Experiments conducted on multiple-host configurations demonstrate stable localization, reliable inter-host communication, and consistent closed-loop control under distributed execution. DMAVA also serves as a foundation for Multi-Vehicle Autonomous Valet Parking, demonstrating its extensibility toward higher-level cooperative autonomy. Demo videos and source code are available at: https://github.com/zubxxr/distributed-multi-autonomous-vehicle-architecture.

Zubair Islam, Mohamed El-Darieby

This paper presents DMV-AVP, a distributed simulation of Multi-Vehicle Autonomous Valet Parking (AVP). The system was implemented as an application of the Distributed Multi-Autonomous Vehicle Architecture (DMAVA) for synchronized multi-host execution. Most existing simulation approaches rely on centralized or non-distributed designs that constrain scalability and limit fully autonomous control. This work introduces two modules built on top of DMAVA: 1) the Multi-Vehicle AVP Coordination Framework, composed of AVP Managers and a per-vehicle AVP Node, is responsible for global parking state tracking, vehicle queuing, parking spot reservation, lifecycle coordination, and conflict resolution across multiple vehicles, and 2) the Unity-Integrated YOLOv5 Parking Spot Detection Module, that provides real-time, vision-based perception within AWSIM Labs. Both modules integrate seamlessly with DMAVA and extend it specifically for multi-vehicle AVP operation, supported by a Zenoh communication layer that ensures high data accuracy and controllability across hosts. Experiments conducted on two- and three-host configurations demonstrate consistent coordination, conflict-free parking behavior, and scalable performance across distributed Autoware instances. The results confirm that the proposed DMV-AVP supports cooperative AVP simulation and establishes a foundation for future real-world and hardware-in-the-loop validation. Demo videos and source code are available at: https://github.com/zubxxr/multi-vehicle-avp

Zubair Islam, Ahmaad Ansari, George Daoud et al.

The fast development of technology and artificial intelligence has significantly advanced Autonomous Vehicle (AV) research, emphasizing the need for extensive simulation testing. Accurate and adaptable maps are critical in AV development, serving as the foundation for localization, path planning, and scenario testing. However, creating simulation-ready maps is often difficult and resource-intensive, especially with simulators like CARLA (CAR Learning to Act). Many existing workflows require significant computational resources or rely on specific simulators, limiting flexibility for developers. This paper presents a custom workflow to streamline map creation for AV development, demonstrated through the generation of a 3D map of a parking lot at Ontario Tech University. Future work will focus on incorporating SLAM technologies, optimizing the workflow for broader simulator compatibility, and exploring more flexible handling of latitude and longitude values to enhance map generation accuracy.