Planning and decision-making are the core functions of an autonomous vehicle for driving safely and efficiently under different traffic scenarios. As discussed in [13], the decision-making and planning algorithms for autonomous vehicles aim to solve problems like (a) determining the future path, (b) utilizing observations of the surrounding environment using the perception system, (c) acting properly when interacting with other road users, (d) instructing the low-level controller of the vehicle and (e) ensuring that autonomous driving is safe and efficient. Therefore, planning and decision-making are very important for autonomous driving. Depending on the traffic scenario, autonomous driving functions are designed for highway driving, off-road driving, or urban driving. Research on highway driving and off-road driving has been going on for a long time with many results on planning and decision-making. Due to the complexity of the urban traffic scenario, decision-making and planning for the urban traffic environment have always been very challenging, with many unsolved problems remaining.

The complexity of the urban traffic scenario is manifested in the following aspects, which are discussed next [14]. The first problem is the presence of a large variation in road types. Roads in a highway scenario are quite similar. Vehicles should either stay in their lane or execute lane changes if needed. Unlike a highway, an urban traffic scenario is composed of different road types, including lanes, intersections, traffic circles and roundabouts as well as lanes for bicyclists and crosswalks for pedestrians. Hence, decision-making and planning need to include intersection management and driving on different types of roads including one-way ones. The second problem is the presence of many different types of road users who share the road. In an urban traffic scenario, the road users are not only vehicles but also vulnerable road users (VRUs) such as bicyclists, pedestrians, scooterists, etc. Much more damage will be caused when VRUs are involved in traffic accidents. The safety of driving, thus, is what the autonomous vehicle and its planning algorithm should always prioritize. Then, there are intersections that may or may not be signalized. Decision-making is easier when the intersection is signalized and there are also traffic lights [15]. The rules of interaction in a signalized intersection with stop signs are also well-established, but the interpretation or lack of knowledge of these rules by drivers may make it difficult for an autonomous vehicle to handle the intersection. Handling a round intersection is always a more challenging task for an autonomous vehicle as decision-making requires knowledge of the other vehicles in the intersection and prediction of their intent. This paper focuses on the decision-making of autonomous vehicles in round intersections and is motivated by the difficulty of autonomously handling the two round intersections by the AV shuttles of the recent Linden LEAP deployment in Columbus, Ohio, U.S.A., as part of its Smart Columbus project [16].

An intersection is a junction where roads meet and cross. One of the major challenges of autonomous vehicle decision-making in urban traffic is handling intersections, especially round intersections that are not signalized. This paper, therefore, focuses on autonomous vehicle decision-making and planning in round intersections. A round intersection is a special case of unsignalized intersections. Intersections, based on the existence of traffic signals, are usually categorized into two types: signalized intersections and unsignalized intersections. The signalized intersection is a centralized control system where the traffic flow is controlled by traffic signals, either traffic lights or traffic signs. Thus, vehicles will behave according to the traffic signals and do not require extra decision-making or behavior planning. In contrast, at an unsignalized intersection, decision-making and planning are decided by the driver or the planner of an autonomous vehicle for determining their behavior when approaching the intersection as well as for interacting with other nearby road users or the traffic within the intersection zone.

According to [17], regular intersections are replaced with round intersections to improve traffic efficiency and safety [18], but there are still traffic problems due to merging and diverging operations, which are relatively easier for a human operator compared with an autonomous vehicle due to the difficulty of the decision-making process. Motivated by this, researchers like [19] have tried to mimic human decision-making by using imitation learning, for example. Decision trees were used in [20] to model human decision-making in handling round intersections. However, handling round intersections is not very easy for human drivers either, according to [21]. This is due to the difficulty in detecting and tracking the other vehicles in the round intersection due to the round geometry and occlusions of view [22,23]. The Vehicle-to-Vehicle (V2V) communication to detect and track all vehicles in a round intersection, as proposed in [24], will obviously help with this measurement problem, but this requires all vehicles to be equipped with V2V modems, which is not the case currently. Even if all vehicles had V2V communication capability, partial observability of the other vehicles would still result from accuracy problems in localization sensors used and communication problems like latency and packet loss [25]. According to [26], drivers are still not accustomed to round intersections and have problems in the form of unpredictable decisions. Deep reinforcement learning was used in [27] to solve this problem, but the training of a deep reinforcement learning control system is very time-consuming and also depends a lot on the intersection geometry used.

The effect of the driving behavior of an AV on round intersection travel performance is investigated in [28]. The driving behavior is discretized into three categories: aggressive, normal and conservative. Low-level driving behavior in the form of low-level actuator controls is studied in many other papers. For example, the authors of [29] designed a model predictive controller speed profile for an ego vehicle to avoid collision with other vehicles in a round intersection assuming full knowledge of the motion of the other nearby vehicles. They used high-fidelity CarSim simulation for the ego vehicle. Ref. [30] also designed a model predictive controller for the round path tracking problem in a round intersection and similarly used CarSim and lower fidelity Simulink models in simulation evaluation for path tracking performance. The incorporation of higher fidelity longitudinal, lateral and vertical suspension dynamics [31] will be useful for the evaluation and validation of the lower-level actuator controls of the ego AV. However, this high-fidelity modeling is not required for higher-level decision-making, which is the focus of the current paper. Here, it is assumed that the low-order actuator controllers for trajectory tracking have already been designed and are part of the lower level of control.

A microscopic traffic model considers individual vehicles with car following, traffic rule obeying and lane changing models within a mathematical model of the road network under analysis. There are many microscopic traffic simulation tools like Vissim, SimTraffic, AIMSUN, CUBE Dynasim, SUMO and others, according to [32], where the commercially available tool Vissim was used for round intersection analysis. A microscopic traffic simulator was used to evaluate the traffic flow capacity of a round intersection in [33]. SUMO was used in [34] for a gap acceptance analysis for round intersections. SUMO was also used in [35] for centralized intersection management at an intersection, curvilinear decision-making for a two-lane round intersection in [36] and cooperative perception analysis in a round intersection in [37]. In accordance with these references, SUMO is used for microscopic traffic simulation in the two-vehicle and multiple-vehicle simulations of this paper. It is also widely used by many researchers and is freely available.