Autonomous vehicles as local traffic optimizers

Ashna Bhatia, Jordan Ivanchev, David Eckhoff, Alois Knoll

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

This paper explores the interaction between autonomous and human-driven cars on a microscopic level using an agent-based traffic simulator. More specifically, it deals with the design of driving logic models of “socially-aware” autonomous vehicles that can improve the performance of surrounding vehicles on the road. Congestion waves, which are created as a result of an abrupt stopping or a car joining a highway, are a known phenomenon in current traffic systems. Experiments performed, demonstrate how the presence of intelligent social vehicles on the road can reduce such effects by acting as a flexible medium between human-driven cars. Metrics to evaluate benefits ot our AV behaviour models under various states of traffic conditions/congestion are also proposed. Finally, results showing the effectiveness of these models are presented.

OriginalspracheEnglisch
TitelComputational Science – ICCS 2020 - 20th International Conference, Proceedings
Redakteure/-innenValeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot, Jack J. Dongarra, Sérgio Brissos, João Teixeira
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten499-512
Seitenumfang14
ISBN (Print)9783030503703
DOIs
PublikationsstatusVeröffentlicht - 2020
Veranstaltung20th International Conference on Computational Science, ICCS 2020 - Amsterdam, Niederlande
Dauer: 3 Juni 20205 Juni 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12137 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz20th International Conference on Computational Science, ICCS 2020
Land/GebietNiederlande
OrtAmsterdam
Zeitraum3/06/205/06/20

Fingerprint

Untersuchen Sie die Forschungsthemen von „Autonomous vehicles as local traffic optimizers“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren