Real-time Management of Large Fleets of Self-Driving Vehicles Using Virtual Cyber Tracks

Arizona State University, Scottsdale AZ

Investigators

Abstract

It is only a matter of time before the transportation infrastructure of freeways, roads, and traffic control systems must accommodate self-driving vehicles (SDVs) at the same time as manually driven vehicles (MDVs). In large scale systems that exist in nearly all metropolitan areas, the question is how can we efficiently, reliably and safely accomplish this? This project aims to generate fundamental knowledge needed to design such a system. More specifically, the project will study and develop decision models and algorithms, and attendant decision-support systems to manage, in real time, large fleets of SDVs and MDVs on the current infrastructure, without the need to construct special roads or guideways. This project will assume that SDVs are cyber-connected and that, through cyber mechanisms of computing and communication, it is possible to guide these SDVs, both individually and in platoons, on our transportation infrastructure efficiently, without sacrificing comfort, safety and efficiency in mobility. The fundamental concepts in the decision-support architecture are (a) controlling directions, speeds and stops to individual SDVs in real-time, (b) grouping SDVs in platoons, (c) moving SDVs in platoons, with short and uniform headways, using the concept of cyber-enabled virtual tracks on the roads, and (d) providing traffic signals on the roads and blocking control (platooning sizing and dispatching) on the virtual tracks to maximize throughput and other desirable traffic performance measures. In addition to the tools developed for operating SDVs in real time, this project will help transportation agencies to efficiently satisfy increasing transportation demand with limited road infrastructure expansion and constrained road capacity. Finally, the research and tools will be integrated into the current and new courses and laboratories for computer science, operations research, and transportation engineering courses. This project will address fundamental knowledge in networking self-driving agents at extremely large scales to meet temporally and spatially distributed traveler demand. The goal is to develop a set of new models for integrated traveler mobility optimization and multi-agent-based control under the new environment of shared SDV networks. It will investigate a novel cyber-track based concept and methods that optimally provide real-time guidance to meet temporally and spatially distributed traveler demand for SDVs (from origins, to intermediate platoons, to destinations), possibly leading to new large scale nonlinear optimization methods that include vehicular dynamics and safety/comfort consideration. By taking full advantage of distributed computing power associated with connected SDVs, the dispatching and operating system for SDVs will simultaneously route and control individual SDVs and platoons on existing highways and streets. Based on a space-time cyber track network modeling framework for representing physical transportation system with constraints, the project will also develop real-time algorithms for proactive control of traffic supply infrastructure (e.g., traffic signals, ramp meters, and traffic information and congestion pricing) that optimizes delays and other performance metrics for both SDVs and MDVs. The project will integrate parallel computing and hierarchical system control, as well as a wide range of real-time vehicle routing/scheduling algorithms, including vehicle routing for platooning, block control and timetabling, to ensure the safety, efficiency and reliability of SDV operations. The project will also study the computational tractability of large scale deployment of SDVs using tools of cloud computing, big data management and parallel computation. The project will develop protocols of collecting steaming data from connected SDVs and managing, distributed computing, and effective logistics for SDV fleets.