This NSF Cyber-Physical Systems (CPS) project will support research that intends to enhance the operation of automated vehicles (AV) swarms in various traffic environments, including structured intersections (e.g., intersections in the US) and unstructured intersections (e.g., intersections in India). The project will examine the 'Tragedy of the Commons (ToC)'? a situation where AVs, while smart on their own, might cause significant traffic oscillation and disorder when they all use the same logic. The research will also examine human-like 'Emergent Cooperative Behavior (ECB)' in complex traffic situations, aiming to incorporate such cooperative driving behaviors into AV control systems and hoping to achieve efficient operation in multi-agent systems. This project could lead to cutting-edge advancements in collaborative control and management in various traffic environments with AVs, which is crucial as they become more common on our streets. Moreover, insights gained from understanding the 'Tragedy of the Commons' and 'Emergent Cooperative Behavior' in AVs could apply to a broader range of multi-agent systems, potentially influencing fields as varied as financial market algorithms or robotics. The collaboration between the U.S. and India in this endeavor not only paves the way for improved global road performance but also sets new international benchmarks. <br/><br/>The primary technical objective of this research is to transition the control and interaction patterns of AVs from the ToC to ECB in diverse traffic environments. The hypothesis that AV control exhibits the ToC such that a stream of AVs may have inferior performance (e.g., causing traffic breakdown) even when an individual AV performs superior (e.g., with less instability) will first be tested. The counterpart of the hypothesis is that human-driven vehicle (HV) behavior manifests the ECB such that while an individual HV may not perform as well, a stream of HVs may maintain reasonable performance even in adverse conditions. This hypothesis will be tested by investigating the Markovian and uniformity properties of AVs and HVs. The Markovian property indicates that the vehicle action is determined by the current traffic state independent of its previous experience, while uniformity implies that vehicles follow a similar behavior rule regardless of the traffic environment. The conjecture that AVs have these properties while HVs do not will be evaluated using field data. How these properties would lead to TOC for AVs yet ECB for HVs with the be examined using analytical modeling and simulation. The finding of these fundamental properties will be used to construct a meta-learning-based AV control approach applicable to both US and Indian intersections, with the intention of reversing the ToC phenomenon for existing AVs and cultivating new AV control using an ECB system.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.