A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

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 negati...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-02, Vol.21 (4), p.1523
Main Authors: Smirnov, Nikita, Liu, Yuzhou, Validi, Aso, Morales-Alvarez, Walter, Olaverri-Monreal, Cristina
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
Clinical decision making
Cooperation
Datasets
Decision making
Decision theory
Driving
Game theory
intelligent transport systems
Intersections
lane change
Lane changing
Maneuvers
Prediction models
Roads & highways
traffic jam
Traffic signals
Vehicles
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21041523