# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 2 Ecosystem Containers
### Infrastructure Container  
The infrastructure container is responsible for tracking each vehicle to which a resource was assigned and determining when a vehicle uses it or waits for it by modifying the mission of the vehicle that requests the connection. These modifications come in the form of ABP suggestions for the effective arbitration to take place using the behavior field mentioned in previous sections. The following modules are required for this purpose:  
Figure 1: Traffic Scene elements.  
* **High-Definition Map module**: A specific implementation of an _AD Map_ module is needed for the system to have a notion of relevant roads and lanes to survey. From the traffic network stored in an ASAM Opendrive [36] file, the infrastructure container extracts planning and navigation information to track new connected vehicles.
* **Perception Module**: At least one set of sensors for perception and localization deployed in the infrastructure is needed. A GNSS positioning system is required for the system to prune non-relevant roads in the traffic network. A Camera-LiDAR-based perception system able to track and classify incoming vehicles is recommended in order to match those connected to the ones perceived [42].
* **Tracking instance**: A tracking node assigned to each vehicle that requests a new connection is required for tracking features. Connections are stored as active nodes in an intermediary queue and then processed on demand.  
Figure 2 shows how the traffic emplacement (intersection) is deployed and sensorized with multiple cameras and a LiDAR sensor prepared for data fusion in real time. Several fusion algorithms exist, each with trade-offs, including deep learning models like DeepFusion [43], TransFusion [44], and BEVFusion [45]. These algorithms employ different fusion strategies such as raw sensor data fusion, independent detection fusion, or final prediction fusion. The main goal of this awareness set up is to provide a perception model with enough accurate contextual information to detect, track and classify incoming vehicles and then integrate those detections into the internal tracking system of the arbitration component, fusing information coming from the cooperative system and the perception inference presented as final perception events, presented in [33], associated with a score representing the uncertainty of the measurement. This effectively allows the management system to implement a more robust tracking methodology based on multiple information sources distributed throughout the network of cooperative systems.  
On the other hand, the localization module integrates Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data, fused via a Kalman filter [46] to yield high-accuracy pose estimates enhanced by Real-Time Kinematic (RTK) corrections. This configuration enables the management system to establish accurate localization with respect to an East-North-Up (ENU) origin, facilitating the precise generation of world frame information for virtual environment construction. Finally, after describing the arbitration ecosystem components, in the next sections, the purpose, responsibilities and  
Figure 2: Heavily sensorized traffic emplacement.  
interaction for each relevant module are going to be described. These modules are the foundation of the collaborative processes that will take place during the arbitration.