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Article: Eliminating Uncertainty of Driver's Social Preferences for Lane Change Decision-Making in Realistic Simulation Environment

TitleEliminating Uncertainty of Driver's Social Preferences for Lane Change Decision-Making in Realistic Simulation Environment
Authors
KeywordsAutonomous vehicle
driving preferences
game theory
lane change
Issue Date2024
Citation
IEEE Transactions on Intelligent Transportation Systems, 2024 How to Cite?
AbstractThe task of making lane change decisions for autonomous vehicles in mixed traffic is intricate and challenging due to the uncertainty of surrounding vehicles. The uncertainty exists in terms of the diverse social driving preferences and unpredictable driving behavior of human drivers. To address these challenges, the decision-making process for changing lanes is represented as an incomplete information game, where the driver characteristics of surrounding vehicles are unknown during the interaction. To eliminate the uncertainty of the driving environment, the concept of driver aggressiveness is proposed to quantify the social driving preferences based on the Risk-Response (R-R) diagram in an explainable manner. Then the predicted trajectory is utilized to calculate the driving risks using Gaussian Mixture Model (GMM) that is trained by the naturalistic driving data in the interactive lane change scenarios extracted from the highD dataset. To make the simulation environment more diverse and realistic, the data-driven motion model social Intelligent Driver Model (SIDM) is constructed based on car-following data obtained from cut-in scenarios in the highD dataset. The simulations are conducted by setting up the environment vehicles equipped with SIDM model with diverse social driving preferences. The findings indicate that the proposed decision-making model can recognize the category of surrounding vehicles, and in realistic interactive driving scenarios, it can produce adaptive and human-like driving decisions.
Persistent Identifierhttp://hdl.handle.net/10722/353249
ISSN
2023 Impact Factor: 7.9
2023 SCImago Journal Rankings: 2.580

 

DC FieldValueLanguage
dc.contributor.authorDeng, Zejian-
dc.contributor.authorHu, Wen-
dc.contributor.authorSun, Chen-
dc.contributor.authorChu, Duanfeng-
dc.contributor.authorHuang, Tao-
dc.contributor.authorLi, Wenbo-
dc.contributor.authorYu, Chao-
dc.contributor.authorPirani, Mohammad-
dc.contributor.authorCao, Dongpu-
dc.contributor.authorKhajepour, Amir-
dc.date.accessioned2025-01-13T03:02:51Z-
dc.date.available2025-01-13T03:02:51Z-
dc.date.issued2024-
dc.identifier.citationIEEE Transactions on Intelligent Transportation Systems, 2024-
dc.identifier.issn1524-9050-
dc.identifier.urihttp://hdl.handle.net/10722/353249-
dc.description.abstractThe task of making lane change decisions for autonomous vehicles in mixed traffic is intricate and challenging due to the uncertainty of surrounding vehicles. The uncertainty exists in terms of the diverse social driving preferences and unpredictable driving behavior of human drivers. To address these challenges, the decision-making process for changing lanes is represented as an incomplete information game, where the driver characteristics of surrounding vehicles are unknown during the interaction. To eliminate the uncertainty of the driving environment, the concept of driver aggressiveness is proposed to quantify the social driving preferences based on the Risk-Response (R-R) diagram in an explainable manner. Then the predicted trajectory is utilized to calculate the driving risks using Gaussian Mixture Model (GMM) that is trained by the naturalistic driving data in the interactive lane change scenarios extracted from the highD dataset. To make the simulation environment more diverse and realistic, the data-driven motion model social Intelligent Driver Model (SIDM) is constructed based on car-following data obtained from cut-in scenarios in the highD dataset. The simulations are conducted by setting up the environment vehicles equipped with SIDM model with diverse social driving preferences. The findings indicate that the proposed decision-making model can recognize the category of surrounding vehicles, and in realistic interactive driving scenarios, it can produce adaptive and human-like driving decisions.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems-
dc.subjectAutonomous vehicle-
dc.subjectdriving preferences-
dc.subjectgame theory-
dc.subjectlane change-
dc.titleEliminating Uncertainty of Driver's Social Preferences for Lane Change Decision-Making in Realistic Simulation Environment-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TITS.2024.3512784-
dc.identifier.scopuseid_2-s2.0-85213041845-
dc.identifier.eissn1558-0016-

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