MobiWave: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancing
2026-07-27 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a system called MobiWave to help coordinate self-driving vehicle fleets more reliably. Their approach separates different traffic patterns using a special graph method and adapts the system carefully when traffic conditions change, avoiding too many risky updates. This helps the fleet better predict where vehicles are needed and keep the system stable over time. They tested MobiWave on real and simulated data, showing it works better than other methods.
autonomous fleetsgraph waveletstraffic patternsdemand predictionfleet rebalancingmobility driftlayer-selective optimizationspectral driftdispatch policyadaptation
Authors
Xiao Han, Pinbo Wang, Yuanshao Zhu, Guojiang Shen, Xiangjie Kong
Abstract
Autonomous fleets enable mobility platforms to coordinate idle vehicles directly, making fleet-wide rebalancing possible. However, two obstacles limit reliable deployment: overlapping regional and local traffic patterns can hide roads that remain useful for dispatch, and mobility drift can make a trained policy unreliable. Existing spatial aggregation mixes these patterns, while updating all parameters from limited recent data is costly and can damage stable knowledge. We propose \name, a framework that connects a dispatch-oriented multi-scale graph wavelet module with Drift-Guided Layer-Selective Optimization (DGLS). The first module addresses the representation challenge by separating graph-frequency patterns and weighting each scale according to its value for demand prediction and feasible rebalancing. DGLS addresses the adaptation challenge by measuring Dispatch-weighted Spectral Drift, selecting affected layers within a resource budget, and separating short shocks from persistent changes through a drift-aware fast--slow update. Candidate validation further rejects updates that fail to improve held-out dispatch reward without worsening monitored service or safety constraints. Experiments on both real-world datasets and simluated environments demonstrate the effectiveness of \name\ in comparing with state-of-the-art methods. The source code and datasets are available at https://anonymous.4open.science/r/MobiWave-40F8/.