A Holistic Approach to the Energy-Efficient Smoothing of Traffic via Autonomous Vehicles

Amaury Hayat, Xiaoqian Gong, Jonathan Lee, Sydney Truong, Sean McQuade, Nicolas Kardous, Alexander Keimer, Yiling You, Saleh Albeaik, Eugene Vinistky, Paige Arnold, Maria Laura Delle Monache, Alexandre Bayen, Benjamin Seibold, Jonathan Sprinkle, Dan Work and Benedetto Piccoli
Additional contact information
Amaury Hayat: Ecole des Ponts Paristech
Xiaoqian Gong: Arizona State University
Jonathan Lee: University of California at Berkeley
Sydney Truong: Rutgers University Camden
Sean McQuade: Rutgers University Camden
Nicolas Kardous: University of California at Berkeley
Alexander Keimer: University of California at Berkeley
Yiling You: University of California at Berkeley
Saleh Albeaik: University of California at Berkeley
Eugene Vinistky: University of California at Berkeley
Paige Arnold: Rutgers University Camden
Maria Laura Delle Monache: INRIA Grenoble—Rhône Alpes
Alexandre Bayen: University of California at Berkeley
Benjamin Seibold: Temple University
Jonathan Sprinkle: University of Arizona
Dan Work: Vanderbilt University
Benedetto Piccoli: Rutgers University Camden

A chapter in Intelligent Control and Smart Energy Management, 2022, pp 285-316 from Springer

Abstract: Abstract The technological advancements in terms of vehicle on-board sensors and actuators, as well as for infrastructures, open an unprecedented scenario for the management of vehicular traffic. We focus on the problem of smoothing traffic by controlling a small number of autonomous vehicles immersed in the bulk traffic stream. Specifically, we aim at dissipating stop-and-go waves, which are ubiquitous and proven to increase fuel consumption tremendously and reduce. Our approach is holistic, as it is based on a large collaborative effort, which ranges from mathematical models for traffic and control all the way to building infrastructures capable of measuring energy efficiency and providing real-time data. Such an approach allows to clearly set and measure a metric for success in the form of a reduction of at least 10% of fuel consumption using 5% of autonomous vehicles immersed in bulk traffic. The chapter illustrates the overall approach and provides simulation results on a tuned microsimulator for the California I-210.

Date: 2022
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DOI: 10.1007/978-3-030-84474-5_10

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