# Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
## 1 Introduction  
The advent of autonomous vehicles (AVs) [1] represents a transformative leap in transportation technology, promising safer, more efficient, and environmentally friendly mobility solutions. As these vehicles navigate complex urban environments and interact with each other and their surroundings, understanding their behaviours and impacts becomes crucial. Agent-based modelling (ABM) [2] emerges as a powerful computational tool for simulating and analysing the intricate dynamics of AV systems. In recent years, ABM has gained prominence across various disciplines for its ability to simulate the behaviour of individual agents within a system, considering their interactions and emergent collective behaviours. When applied to AVs, ABM offers a unique networking approach to studying the complex interactions between vehicles, pedestrians, infrastructure, and the environment, as shown in Figure 1[3, 4]. Vehicular wireless communications and networks are increasingly recognized as pivotal facilitators for enhancing traffic safety and efficiency. Numerous proposals have emerged advocating [5] for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, aiming to leverage these technologies to their full potential [6]. The concept of a car being able to act as an intelligent agent (IA) was first introduced by World's Fair, with an exhibition that proposed remote controlled electric vehicles propelled by electromagnets [7]. The human factor in driving would be eliminated by the 1960s, with the first truly AVs appearing in the 1980s. Driverless cars are now appearing on ourroads, with Mercedes-Benz being the world's first manufacturer to introduce SAE (formerly known as Society of Automotive Engineers) Level 3 automated driving [8]. The first three levels (Level 0, 1, and 2) represent support features for the drivers, for example, automatic emergency braking, adaptive cruise control, and lane centring. On the other hand, Levels 3, 4, and 5 focus on automated driving features like driver-less cars. Whether fully AVs will be on our roads any time soon, though, is still uncertain due to the high cost of ownership, public perception regarding safety, and issues with key technology enablers. The term 'agent vehicle' is used in this report to denote any vehicle with the capability to make intelligent decisions based on percepts it senses in its environment. In recent years, the field of autonomous driving has witnessed remarkable growth, spanning across various technological domains including radar, cameras, Lidar, object recognition, high-precision mapping, and navigation technology [9; 10; 11; 12]. With its long-standing involvement in the automotive industry, MathWorks has played a pivotal role in advancing autonomous driving technology [13; 14]. ABM stands out as a powerful tool within the network landscape, offering unique capabilities to capture the heterogeneity and adaptability of agents, particularly relevant in the context of AV networking. Unlike conventional modelling approaches which often rely on simplified assumptions or aggregate-level analyses, ABM enables researchers to depict the diverse behaviours and decision-making processes of individual vehicles and how they network with other entities within the environment. One significant advantage of ABM lies in its capacity to simulate various network scenarios and policy interventions, allowing for a comprehensive assessment of the potential impacts of AVs on factors such as traffic flow, safety, energy consumption, and urban planning. By simulating the networking between AVs and their surroundings under diverse conditions, ABM serves as a valuable tool to inform decision-making processes and optimize the design and deployment of AV technologies. However, integrating ABM with AV simulations presents several challenges. These challenges include the requirement for accurate data inputs, scalability of computational resources, and validation against real-world observations.