Autonomous vehicles need to maneuver traffic infrastructures flawlessly to safely get from one destination to another. The multitude of decisions that need to be made at the intersection along with the time sensitive nature of the message results in a computationally complex challenge. The research investigates using Physical layer Security (PLS) to enhance the communication between friendly vehicles while preventing malicious eavesdropping. Keyless PLS exploits the wireless channel characteristics and provides quantifiable metrics average secrecy capacity (ASC) and signal outage probability (SOP). The environment for the communication involves cascaded Rayleigh (CR) Fading with multiple interferers, along with colluding and non-colluding eavesdroppers. This research investigates the gap when applying realistic network conditions in the investigation of SOP and ASC metrics. The research also investigates direct communication and multi-hop communication between one and more multiple friendly vehicles. The analytical methods along with an investigation into the derivation of ASC and SOP metrics with realistic characteristics and multi-hop conditions are investigated. A discussion on the comparison of the analysis and simulation are then given. Then, a method for optimal relay assignment (ORA) for cooperative communication scenarios are given along with their algorithms. The research links together multiple complex topics in the literature: CR fading, multiple interferers, multiple eavesdroppers, and multi-hop communication. It provides in-depth mathematical analysis for how one may simulate or analyze the ASC and SOP under multiple conditions. Through the investigation a case is made for using CR fading. Then, the techniques discussed in this thesis provide a robust investigation on how to ensure secure communication between autonomous vehicles. Finally, the ORA algorithm provides a deeper analysis on how vehicles may communicate to one another at the intersection. This research will benefit engineers, designers, and other stakeholders in the future of autonomous intersection management.