# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 9 Conclusions and Future Work  
In this work, a centralized arbitration system is presented based on behavioral collaboration and multi-agent communication. The idea was implemented on an ROS2 open-source framework and tested on Carla simulator for realistic experimentation. The proposed architecture demonstrated sufficient performance for real-time arbitration in complex traffic environments, making use of fully automated vehicle systems with cooperative capabilities, decreasing the average waiting time in simulated intersection scenarios by 13% and increasing the total utilization of the traffic emplacement by 70% compared to the classic traffic light model. These results are presented taking into account the current simulation test-bed limitations and with at most four CCAVs. Future research will integrate these results as a baseline for framework improvements and enhancement with diverse traffic densities. Furthermore, the proposed framework incorporates inherent extensibility to readily accommodate future implementation requirements and leverage emerging advancements. The framework's modular architecture facilitates the development and seamless integration of novel modules, thereby enhancing system capabilities and enabling operation within increasingly complex traffic environments incorporating pedestrian dynamics. For instance, the integration of a dedicated pedestrian prediction module will augment the system's perceptive and decision-making capacity in scenarios involving crossings. Moreover, the idea of behavior collaboration provides the flexibility to adjust the way CCAVs operate under certain traffic emplacements, potentially decreasing the amount of waiting time, improving traffic prediction, and mitigating congestion that may occur in modern cities. The dynamic nature of the ABP concept allows for adaptive responses that are able to adjust to the current context in a clear and transparent way. And, while the system is in a preliminary phase, it provides a basic foundational framework from which to modulate the available collaborative aspects of a vehicle, conceivably enabling a better driving experience. Although the proposed system focused on Entry and Exit waypoints where a specific behavior is defined, for future works, this idea can be extended to be more dynamic in nature, allowing the behavior itself to be adjusted depending on the information shared among the traffic agents. This can mean small arbitration instances of trajectory optimization, speed/acceleration modulation, and, of course, Entry flag management to dynamically modulate the usage of resources. Hence, by employing fine-grained control strategies (control mechanisms that allow for a very precise and detailed level of influence or management over a system or process) over traffic infrastructure through atomic behavior primitives (the most fundamental, indivisible units of actions inside an ABP that an automated vehicle can execute), the system enhances traffic flow by minimizing non-essential stops. Instead of coarse-grained Entry and Exit behaviors, the system would implement sophisticated strategies that dynamically adjust lane assignments, localized speeds, and trajectories for optimized traffic management. Furthermore, for mixed traffic scenarios, the proposed system can incorporate a human-machine interface (HMI) to provide guidance to human drivers. This HMI, utilizing modalities such as visual, auditory, or non-invasive haptic feedback, would convey optimal driving actions within the managed intersection or round-about. This approach would extend the system's real-time computations beyond internal control, disseminating actionable information to cooperative vehicles via the HMI. Finally, further empirical validation is required across a broader spectrum of traffic conditions, particularly those involving high-velocity ingress.