# Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature/Metric** & \begin{tabular}{c} **Traditional Control** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Agent-Based Modelling** \\ **(ABM)** \\ \end{tabular} \\ \hline \multirow{2}{*}{**Scalability**} & Limited in handling large-scale, dynamic systems & High scalability with multi-agent dynamics \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Emergent Behavior** \\ **Analysis** \\ \end{tabular} } & Not explicitly addressed & Explicit modeling and simulation of emergent behaviors \\ \hline \multirow{2}{*}{**Flexibility**} & Predefined rules, limited adaptability & Flexible and adaptable to changing conditions \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Computational** \\ **Complexity** \\ \end{tabular} } & Efficient for simpler systems & Higher computational demand but allows nuanced modeling \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Decision-Making** \\ **Dynamics** \\ \end{tabular} } & Predictive or rule-based & Interaction-aware, considers agent-level heterogeneity \\ \hline \multirow{2}{*}{**Application**} & Optimal for predefined or predictable scenarios & Suitable for dynamic, stochastic environments \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of traditional control methods and agent-based modelling (ABM). To act intelligently, the AV must be able to choose the best action depending on what it is perceiving in the environment and its built-in knowledge [26]. Currently, vehicles on the road are somewhat controlled by both smart infrastructure (e.g., motorway variable speed limits) and driver assistance features on vehicles (e.g., adaptive cruise control). A key enabler of AVs will be systems that incorporate not only intelligent roadside infrastructure, but also utilise the sensing, control, and communications enablers afforded by intelligent vehicles. A variety of appropriate control methods for AVs exist, as well as the multi-agent traffic systems they will operate in, including static feedback control, optimal control, model predictive control, hierarchical control, and more modern AI techniques [27]. Modern computing power has helped facilitate the increase in AV capabilities and the choice of control method will depend on the nature of the application. As highlighted earlier, motion prediction stands as a fundamental aspect within contemporary robotics and autonomous driving research. Predictably, a variety of methodologies have emerged in recent years, each presenting distinct characteristics in terms of abstraction level. An extended literature survey indicates existing motion prediction methods can be categorised into three distinct silos: Physics-based, Manoeuvrer-based, and Interaction-aware models. This paper discusses and summarizes related works in Section 2. Section 3 discusses the methodology adopted and the novel contributions made by this work, followed by system design and architecture with focus on program and configuration in Section 4. Section 5 discusses simulation setup and analysis and its validation.