Multi-Agent Coordination in Autonomous Vehicle Routing: A Simulation-Based Study of Communication, Memory, and Routing Loops

Multi-agent coordination is critical for next-generation autonomous vehicle (AV) systems, yet naive implementations of communication-based rerouting can lead to catastrophic performance degradation. This study investigates a fundamental problem in decentralized multi-agent navigation: routing loops, where vehicles without persistent obstacle memory become trapped in cycles of inefficient path recalculation. Through systematic simulation experiments involving 72 unique configurations across varying vehicle densities (15, 35, 55 vehicles) and obstacle frequencies (6, 20 obstacles), we demonstrate that memory-less reactive rerouting increases average travel time by up to 682% compared to baseline conditions. To address this, we introduce Object Memory Management (OMM), a lightweight mechanism enabling agents to retain and share knowledge of previously encountered obstacles. OMM operates by maintaining a distributed blacklist of blocked nodes, which each agent consults during Dijkstra-based path recalculation, effectively preventing redundant routing attempts. Our results show that OMM-enabled coordination reduces average travel time by 75.7% and wait time by 88% compared to memory-less systems, while requiring only 1.67 route recalculations per vehicle versus 9.83 in memory-less scenarios. This work provides empirical evidence that persistent, shared memory is not merely beneficial but essential for robust multi-agent coordination in dynamic environments. The findings have implications beyond autonomous vehicles, informing the design of decentralized systems in robotics, network routing, and distributed AI. We provide a comprehensive experimental analysis, including detailed scenario breakdowns, scalability assessments, and visual documentation of the routing loop phenomenon, demonstrating OMM's critical role in preventing detrimental feedback cycles in cooperative multi-agent systems.