# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 8 Discussion  
After the explanation of the solution proposed for traffic arbitration based on behavioral collaboration and the experimentation carried out, some important comments must be made. The solution presented in this work is a basic foundation to represent the possibilities of future arbitration systems that aim to harmonize the interaction between connected traffic agents. The implementation of the idea proved to be valid with an improvement in the waiting time of each managed vehicle and the average idle time of the emplacement. The system focuses on the management of several CCAVs under the same ecosystem and their arbitration in two different traffic topologies, intersections and roundabouts, showing modular and flexible capacities. Finally, the framework presented provides a full ecosystem of well-defined responsibilities for the cooperation of the Intelligent Transportation System powered by the ROS middleware framework and validated in the Carla simulator. Nevertheless, there are still problems to be faced, mostly in the field of vehicular networks and agent communications and also in multi-agent simulation and resource optimization. More testing is needed, and a better and optimized simulation framework for the simulation of real traffic demand that operates under the software container explained is vital for the system to work properly. However, this is a step forward in the generation of an effective framework for arbitration that does not necessarily have to count on the automatic nature of the vehicle. Although one of the main disadvantages of the solution proposed is that it assumes the vehicle requesting the connection is fully automated, in reality, mixed traffic can exist, and the arbitration system should take into account vehicles not operated automatically, as well as pedestrian crossings. In order to carry that out, the proposed system necessitates a holistic architecture addressing safety, efficiency, and intricate vehicle-pedestrian interactions. A dedicated Pedestrian Intention Prediction module, leveraging machine learning models trained on kinematic data (gait, direction, velocity) and spatial context (proximity to crosswalks), is crucial for anticipating pedestrian behavior beyond mere detection. Moreover, prioritizing safety, the arbitration logic must implement a preemptive safety response for any potential pedestrian conflict. Furthermore, the system shall integrate real-time pedestrian signal state awareness, mandating CCAVs to yield upon detection of an active pedestrian signal. These functionalities will be encapsulated within a dedicated Pedestrian Handling Module, integrated into the core arbitration software, responsible for processing pedestrian-centric data and generating corresponding control actions. Furthermore, the selection cost function should be refined, taking into account traffic forecasting parameters to enhance the overall performance of the process. This cost function should incorporate several key features, such as Time-to-Collision (TTC) within defined conflict zones, employing a critical safety threshold; a delay metric computed based on predicted and current waiting times, the cumulative number of stops since arbitration initiation, and the estimated plan execution time. The cardinality of compatible cluster assignments at each time step also has to be considered to maximize resource utilization. Furthermore, the presence of pedestrian crossings and detected pedestrian agents constitutes a critical term in the decision-making process. Finally, the cost shall be modulated by vehicle type, encompassing size and category, to accommodate specific behaviors for emergency vehicles, heavy good vehicles, and standard passenger vehicles. Hence, new implementations should consider these features while the technology evolves and more mature and robust automated systems are constructed. Finally, it is pertinent to address limitations within the simulation environment that directly influenced the experimentation phase.