Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103084
Title: A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios
Authors: Smirnov, N.
Liu, Y.
Validi, A.
Morales-Alvarez, W.
Olaverri-Monreal, C.
Issue Date: 2021
Publisher: MDPI AG
Citation: A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios / N. Smirnov, Y. Liu, A. Validi, et al. — DOI 10.3390/s21041523 // Sensors. — 2021. — Vol. 21. — Iss. 4. — P. 1-20. — 1523.
Abstract: Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: GAME THEORY
INTELLIGENT TRANSPORT SYSTEMS
LANE CHANGE
TRAFFIC JAM
BEHAVIORAL RESEARCH
DECISION MAKING
DECISION THEORY
FORECASTING
GAME THEORY
MOTOR TRANSPORTATION
PREDICTIVE ANALYTICS
ROAD VEHICLES
ROADS AND STREETS
DECISION MAKING MODELS
HUMAN DECISION MAKING
LANE CHANGE MANEUVERS
PREDICTION ACCURACY
PREDICTION MODEL
URBAN INTERSECTIONS
URBAN SCENARIOS
URBAN TRAFFIC SCENARIOS
AUTONOMOUS VEHICLES
URI: http://hdl.handle.net/10995/103084
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85101082610
PURE ID: 21027422
29bccd2b-7047-45b4-bfde-98f4791701b9
ISSN: 14248220
DOI: 10.3390/s21041523
metadata.dc.description.sponsorship: This work was supported by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0., IAV France S.A.S.U., IAV GmbH, Austrian Post AG, and the UAS Technikum Wien. It was additionally supported by the Zero Emission Roll-Out?Cold Chain Distribution_877493.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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