Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems
Matthew Tsao, Dejan Milojevic, Claudio Ruch, Mauro Salazar, Emilio Frazzoli, Marco Pavone
Abstract
This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers' travel time and vehicles' mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.
BibTeX
@inproceedings{icra2019_modelpredictivec,
title = {Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems},
author = {Matthew Tsao and Dejan Milojevic and Claudio Ruch and Mauro Salazar and Emilio Frazzoli and Marco Pavone},
booktitle = {ICRA 2019},
year = {2019}
}