Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow

被引:0
作者
Buckman, Noam [1 ]
Karaman, Sertac [2 ]
Rus, Daniela [1 ]
机构
[1] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA
[2] MIT, Lab Informat & Decis Syst, Cambridge, MA 02139 USA
来源
2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV | 2023年
关键词
VEHICLES;
D O I
10.1109/IV55152.2023.10186563
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. In addition, new autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. Yet the overall impact on traffic flow for this new class of planners remain to be understood. In this work, we present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response assuming knowledge of the SVOs of other agents. We simulate nominal traffic flow and investigate whether the proportion of prosocial agents on the road impact individual or system-wide driving performance. Experiments show that the proportion of prosocial agents has a minor impact on overall traffic flow and that benefits of semi-cooperation disproportionally affect egoistic and high-speed drivers.
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收藏
页数:8
相关论文
共 22 条
  • [1] Semi-Cooperative Control for Autonomous Emergency Vehicles
    Buckman, Noam
    Schwarting, Wilko
    Karaman, Sertac
    Rus, Daniela
    [J]. 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2021, : 7052 - 7059
  • [2] Buckman N, 2019, IEEE INT C INT ROBOT, P6136, DOI [10.1109/iros40897.2019.8967997, 10.1109/IROS40897.2019.8967997]
  • [3] A generic multi-level framework for microscopic traffic simulation with automated vehicles in mixed traffic
    Calvert, S. C.
    van Arem, B.
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 110 : 291 - 311
  • [4] Crosato L., 2021, HUMAN CENTRIC AUTONO, V9
  • [5] Fisac JF, 2019, IEEE INT CONF ROBOT, P9590, DOI [10.1109/icra.2019.8794007, 10.1109/ICRA.2019.8794007]
  • [6] Garapin A, 2015, REV ECON POLIT, V125, P701
  • [7] Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction
    Glaser, Sebastien
    Vanholme, Benoit
    Mammar, Said
    Gruyer, Dominique
    Nouveliere, Lydie
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2010, 11 (03) : 589 - 606
  • [8] Modeling the Impact of Vehicle Platooning on Highway Congestion: A Fluid Queuing Approach
    Jin, Li
    Cicic, Mladen
    Amin, Saurabh
    Johansson, Karl H.
    [J]. HSCC 2018: PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON HYBRID SYSTEMS: COMPUTATION AND CONTROL (PART OF CPS WEEK), 2018, : 237 - 246
  • [9] General lane-changing model MOBIL for car-following models
    Kesting, Arne
    Treiber, Martin
    Helbing, Dirk
    [J]. TRANSPORTATION RESEARCH RECORD, 2007, (1999) : 86 - 94
  • [10] Kuefler A, 2017, IEEE INT VEH SYM, P204, DOI 10.1109/IVS.2017.7995721