Large-Scale Autonomous Vehicle Fleet Management

Abstract

We present a hierarchical framework for city-scale autonomous ride-hailing that integrates vehicle prepositioning, request matching, charging, and facility ingress. A fine-grained mixed-integer program (MIP) coordinates prepositioning and matching on short horizons, while a coarse-grained “Deployment+Summoning” decomposition enforces charger/parking capacities at scale. On ride-hail traces, the method increases coverage and reduces wait relative to greedy and decoupled baselines, while keeping charger overuse near zero under rolling-horizon execution. We detail boundary-condition handling for 24/7 operations and specify a concrete RL training/validation protocol for a constraint-aware hybrid in which learned policies act tactically under a MIP-based safety shield.