###### Abstract  
The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the lack of works studying the impact that the communication network can have on the decentralized control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based AIM for the 5G communication network, called AIM5 LA. AIM5 LA is the first AIM that considers the inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent Deep Reinforcement Learning (MADRL), thus obtaining a robust and resilient multi-agent control policy. Beyond considering the latency history experienced, AIM5 LA predicts future latency behavior to provide enhanced security and improve traffic flow. The results demonstrate huge safety improvements compared to other AIMs, eliminating collisions (on average from 27 to 0). Further, AIM5 LA provides comparable results in other metrics, such as travel time and intersection waiting time, while guaranteeing to be collision-free, unlike the other AIMs. Finally, compared to other traffic light-based control systems, AIM5 LA can reduce waiting time by more than 99% and time loss by more than 95%. 5G latency forecasting; 5G vehicular communication; autonomous intersection management; autonomous vehicles; connected autonomous vehicles; cooperative autonomous driving; intelligent transport systems; intersection traffic management; latency-aware driving; unsignalized intersection +
Footnote †: journal:  
Acticle  
AIM5 LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection-  
obtain their status, AIMs communicate with the vehicles to obtain multiple data such as geographical position, current speed, acceleration, orientation, route, etc. In addition, AIMs can integrate data from external sensors [2; 3] such as cameras, noise sensors, pollution sensors, etc. Once all the data is obtained, AIM evaluates the control policy and obtains the action (next target speed, acceleration, route, etc.) that each AV should take to ensure safety and maximum flow. Figure 1 shows a representation of an AIM. Note that AIM refers to the control algorithms of the AVs and IM to the node in charge of the communication between the AVs and the AIM. In this context, the control of AVs using AIM requires knowledge of the state of all AVs to be controlled to make the right decisions, as well as AVs cooperating to achieve the same goals. A cooperative control system must be aware of the state of all AVs at the intersection and adapt its strategy in a time-limited manner. In addition, it must have the necessary foresight to be able to anticipate potential problems and cope with uncertainties. To the authors' knowledge, most research studies on cooperative control in AIM focus on the design of control strategies, and do not consider (or mostly assume ideal conditions) the communication latencies inherent in wireless communication systems between AVs and the IM. Due to the lack of works studying the impact that the communication network can have on vehicle control and safety, this paper presents a novel latency-aware deep reinforcement learning-based AIM used for AV control in the 5G communication network, called AIMSLA. It was decided to use the 5G communication network given the advantages it offers for vehicle control using Ultra Reliable Low Latency Communications (URLLC). This is the first latency-aware AIM trained fully by Multi-Agent Deep Reinforcement Learning (MADRL).