Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: a game theoretic approach

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dc.contributor.authorFang, Shiyu
dc.contributor.authorHang, Peng
dc.contributor.authorWei, Chongfeng
dc.contributor.authorXing, Yang
dc.contributor.authorSun, Jian
dc.date.accessioned2024-06-18T13:47:07Z
dc.date.available2024-06-18T13:47:07Z
dc.date.issued2024-05-13
dc.description.abstractHigh-density, unsignalized intersections have always been a bottleneck of efficiency and safety. The emergence of Connected Autonomous Vehicles (CAVs) results in a mixed traffic condition, further increasing the complexity of the transportation system. Against this background, this paper aims to study the intricate and heterogeneous interaction of vehicles and conflict resolution at the high-density, mixed, unsignalized intersection. Theoretical insights about the interaction between CAVs and Human-driven Vehicles (HVs) and the cooperation of CAVs are synthesized, based on which a novel cooperative decision-making framework in heterogeneous mixed traffic is proposed. Normalized Cooperative game is concatenated with Level-k game (NCL game) to generate a system optimal solution. Then Lattice planner generates the optimal and collision-free trajectories for CAVs. To reproduce HVs in mixed traffic, interactions from naturalistic human driving data are extracted as prior knowledge. Non-cooperative game and Inverse Reinforcement Learning (IRL) are integrated to mimic the decision-making of heterogeneous HVs. Finally, three cases are conducted to verify the performance of the proposed algorithm, including the comparative analysis with different methods, the case study under different Rates of Penetration (ROP) and the interaction analysis with heterogeneous HVs. It is found that the proposed cooperative decision-making framework is beneficial to driving conflict resolution and the traffic efficiency improvement of the mixed unsignalized intersection. Besides, due to the consideration of driving heterogeneity, better human-machine interaction and cooperation can be realized in this paper.en_UK
dc.identifier.citationFang S, Hang P, Wei C, et al., (2024) Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: a game theoretic approach. IEEE Transactions on Intelligent Vehicles. Available online 13 May 2024en_UK
dc.identifier.eissn2379-8904
dc.identifier.issn2379-8858
dc.identifier.urihttps://doi.org/10.1109/TIV.2024.3399694
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22508
dc.language.isoen_UKen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectconnected autonomous vehiclesen_UK
dc.subjectheterogeneous mixed trafficen_UK
dc.subjectunsignalized intersectionen_UK
dc.subjectlevel-k gameen_UK
dc.subjectinverse reinforcement learningen_UK
dc.subjectcooperative drivingen_UK
dc.titleCooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: a game theoretic approachen_UK
dc.typeArticleen_UK

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