<![CDATA[The study addresses the limitations of the Ad Hoc On-Demand Distance Vector (AODV) protocol in vehicle-to-vehicle (V2V) communication, explicitly targeting issues such as low data transfer rates, increased delay times, reduced throughput, and data congestion due to dynamic network topologies. The research introduces a novel protocol called Learning Automata Ad Hoc On-Demand (LAAODV) to enhance these areas. Utilizing NS3 and SUMO for dynamic traffic simulations, LAAODV demonstrated superior performance compared to AODV. Key findings include a higher packet delivery success rate with a Packet Loss Ratio (PLR) of 95%, lower than AODV's 96%, and a Packet Delivery Ratio (PDR) of 4.5% compared to AODV's 3.25%, indicating its effectiveness in reducing packet loss. The study also highlights significant improvements in PDR and Average Throughput, showcasing LAAODV's enhanced performance in dynamic traffic conditions. LAAODV provides an effective solution to the shortcomings of existing routing protocols, significantly enhancing V2V network performance. This research underscores the importance of developing robust and adaptive routing solutions to meet the evolving demands of dynamic vehicular environments, contributing to more efficient and reliable V2V communication protocols.]]>
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