# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 1 Introduction  
The industry of automated vehicle systems has been operating for decades, providing novel solutions and mechanisms to cope with current problems related to traffic in modern cities. Advancements in advanced driver assistance systems (ADASs) [1, 2] have provided human drivers with supportive tools to enhance the driving experience and improve road safety while maintaining a fine balance between responsibilities. For some time now, the industry has developed automated solutions that aim to provide a full level 5 technologicalplatform capable of driving better and safer than any human driver [3; 4]. However, there are still many challenges to be faced in this field [5], and the more advancements that occur, the more potential flaws, opportunities, and paths are discovered to improve the current technology used. Generally speaking, automated driving is approached primarily by current state-of-the-art solutions from a selfish perspective in which the flow of information is local to the vehicle being controlled. The encapsulation of this information can lead to complex traffic situations with partial information. If poorly assessed, these situations can result in potentially unsafe decisions that can threaten other road users or the passenger's life. A practical example of the previous fact is that real-world inference works over assumptions and hypotheses [6], not providing fail-safe mechanisms with enough accuracy to cope with occlusions and blind spots of highly inaccurate measurements. Every sensor solution for real-world inference suffers from flaws related to the context and usage of readings from LiDAR and radar technologies to camera detection or GNSS localization. From past errors and mistakes, as well as unpredictable accidents, the industry is forced to evolve and learn by providing better solutions to cope with current limitations [7]. Therefore, recognizing the challenges of insufficient information in traffic management, Connected Cooperative Automated Mobility (CCAM) [8] provides a solution for improved distributed control and reduced risk of critical errors. CCAM is expected to render a huge change in current technological solutions that only focus on selfish information. The capabilities to implement a distributed system aimed at collaborating and/or arbitrating between connected vehicles are the first step to solving the limitations previously explained. As a consequence, new and promising solutions for vehicular networks and V2X communications are providing a ground basis from which to generate the future communication layer for this to happen [9; 10]. However, while these advancements take place, providing robust methodologies for communication between vehicles in future scenarios, some interesting research can be performed in parallel, hypothesizing about the possibilities of arbitration in current city environments. This is because not only is sharing perceptual information important, but so are the collaborative processes that can take place in complex traffic emplacement to enhance the traffic flow, which can potentially prevent unnecessary bottlenecks or improve routing for emergency situations. Because vehicular networks are still under development, defining a practical operational environment requires establishing certain constraints. Current efforts at the software level focus on using deep learning models to meet communication allocation needs [11; 12], while hardware limitations are still a problem that requires constant testing [13]. However, while advancements progress, further development of traffic agent arbitration and collaboration will establish a strong foundation for future benefits. Conventional connected and automated vehicle (CCAV) coordination necessitates human intervention, employing a driver-in-the-loop hybrid cooperation model [14]. Haptic feedback mechanisms, including visual or auditory signals, directly cue the driver to facilitate coordination with other traffic participants.