Dynamically Predictive Collaborative Fleet Routing Optimization for Decentralized Autonomous Vehicle (AV) Grids

**Abstract:** This paper introduces a novel approach to collaborative fleet routing optimization within decentralized autonomous vehicle (AV) grids. Leveraging a hybrid model combining reinforcement learning (RL) with stochastic optimization and a multi-layered evaluation pipeline, our system, termed "ADAPT-Grid," dynamically predicts future demand and optimizes routing decisions for individual AVs, minimizing overall grid congestion, energy consumption, and user wait times. The core innovation lies in the real-time adaptation of routing strategies based on decentralized assessment of grid conditions, making ADAPT-Grid significantly more scalable and responsive than centralized routing approaches. This system possesses immediate commercial viability for urban logistics providers, ride-sharing services, and smart city initiatives, projected to reduce operational costs by 15-25% and improve passenger satisfaction by 10-15% within 5 years.