## 2 Related Works  
AIMs represent a very suitable approach to solving traffic problems because of the great versatility they offer. They have been extensively studied and have demonstrated an ability to reduce accidents and increase traffic flow by decreasing congestion and delays at urban intersections. The first AIMs were proposed by [PERSON] et al. in [5; 6; 7; 8; 9]. These AIMs were the first Query-Based (QB) methods applied to AVs control at intersections. The operation of the proposals presented by [PERSON] et al. was based on the vehicle attempting to cross the intersection requesting the IM if it could pass (or not), indicating its estimated time of arrival. The IM could accept the request if there were no collisions with other vehicles based on the Estimated Time of Arrival (ETA). Otherwise, the IM would send a Denial of Request (DoR), and the VA would have to slow down and ask again later. This approach was based on the First-Come-First-Served (FCFS) policy and outperformed traditional traffic light control systems in low traffic flows (<500 veh/h). Since FCFS presented several serious problems that could lead to risky situations [10], [PERSON] et al. proposed in [11] a QB alternative that allowed grouping vehicles according to their status and allowed the IM to indicate the recommended deceleration speed when there was a DoR. The results showed a reduction in time loss of about 85%, as well as a reduction in fuel consumption of 50%. Other FCFS improvements were those developed by [9; 12], through which it was possible to create platoons of vehicles traveling in the same direction, increasing the intersection flow. The results obtained improved the flow of vehicles by \(\times 2\) and reduced the time loss by 85% compared to traditional traffic light control techniques. An alternative to FCFS was suggested by [13], in which the right-of-way policy was calculated based on the fastest vehicle arriving at the intersection, called Fast First Service (FFS). The findings were superior to traffic light-based techniques, allowing a reduction in hard-braking. Other control techniques, in addition to FCFS, use mathematical optimizations to determine the passing priority policy [14; 15; 16; 17; 18]. Although the results obtained with these techniques were similar to those obtained previously in work using FCFS, the high complexity of large intersections makes a real-time control solution unfeasible. A control policy different from FCFS was proposed by [PERSON] et al. in [19], where the proposed policy was auction-based. The vehicles that bid the most to cross were the ones that could pass through the intersection first, creating a market economy. Although the results shown were superior to those of the compared algorithms, the problems derived from this economy could generate vehicle starvation, which could suffer indefinitely long waiting times, as well as generate inflation, discrimination, etc. Some works use Reinforcement Learning (RL) to address this approach. For example, the works proposed in [10; 20] used RL to obtain the vehicle passing priority policy, demonstrating excellent performance when compared to other control algorithms and reducing waiting time by more than 80%. Another work using Deep Reinforcement Learning (DRL) was presented in [4], where DRL was used to obtain an advanced vehicle control policy. In this case, the IM used DRL to find the optimal actions to be taken by the vehicles (the speed to which the vehicles should adapt at each time interval) when approaching (and within) the intersection. In that proposal, it was necessary to use training acceleration techniques such as curriculum learning [21] and Twin Delayed Deep Deterministic policy gradient (TD3, [22]) to reduce the training time because the training scenario could be considered highly complex. The results obtained showed excellent performance in multiple metrics, such as a reduction in time loss of up to 95%, reductions in pollutant gas emissions of around 50%, or reductions in fuel and energy use of 25%.