## 1 Introduction  
The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., healthcare, autonomous driving, etc. Statistics show that more than one million people are killed in traffic accidents yearly, and the vast majority of the accidents are caused by human negligence [1]. Therefore, the realization of safe and robust autonomous driving (AD) has the potential to drastically reduce road traffic accidents, congestion, and excessive fuel consumption by shifting the driving control from humans to autonomous vehicles. The Society of Automotive Engineers (SAE) [2] classifies autonomous vehicles into six levels of autonomy. Most of the autonomous driving features that are currently commercially available in Level (L) 2 and L3 autonomous vehicles (AVs) are Advanced Driving Assistance Systems (ADAS) and were first deployed in structured and controlled environments such as highway driving and low-speed parking. However, in urban and uncontrolled environments, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, cyclists, and dynamic unprecedented events make it challenging for the classical AV to safely navigate the traffic, reduce travel delay, and avoid congestion. Therefore, the classical autonomous driving of L2 and L3 are pushing toward the L5 fully autonomous driving. In relation to this, both industry and research communities have been working on and contributing solutions such as collaborative autonomous driving to realize a higher level of autonomous driving. Collaborative autonomous driving solutions have been around for some time. However, the focus has been on lower automation levels and vehicular networks. Behavioral planning, local planning, and inter-vehicular communication for lower automation levels are simpler when compared with the envisioned higher automation levels that involve different weather dynamics, congested settings, many lanes populated with vehicles of varying speeds, roundabouts, crossings, pedestrians, etc. These complex dynamics ask for the collaboration of short-time quanta and a varying number of collaborating vehicles. The solution to these challenges necessitates a newer design goal of the evolved version of collaborative autonomous driving. However, an obvious challenge in this regard is how to create a dynamic and uncertain environment that realistically captures the complex dynamics of this problem domain. Thus, to create and validate the solutions to these and similar problems, a realistic environment is required for which the researchers heavily rely on different options such as (i) simulators, (ii) emulators, and (iii) real environment. Testing autonomous vehicles in real-time on public roads has always been extremely challenging due to the high cost of the required infrastructure, high-performance computing, sensors, and communication equipment, jeopardizing public safety. This is where simulation testing helps to fill the research gap and democratize autonomous driving research. In this research, we opt for simulation-based testing to define, model, and simulate the dynamic and uncertain environment that realistically captures the dynamics of a complex and urban environment. There are a bunch of simulators that are being used by researchers in this area such as RRADS [3], TORCS, and Udacity [4]. However, these simulators lack the environmental dynamics and complexity, such as predicting and controlling the behavior of pedestrians, road junctions, traffic laws, and other complex dynamics, that differentiate urban driving from simple track racing. In addition, some closed-source commercial simulators such as Grand Theft Auto V and ANSYS offer little environment customization and control, restricted kinematic behavior, limited scripting, and use case scenario descriptions, constrained sensor suite specification, and various other limitations caused by the commercial nature of the simulator.