A Combinatorial Dynamic Network Trajectory Reservation Algorithm for Connected Autonomous Vehicles

被引:3
|
作者
Levin, Michael W. [1 ]
机构
[1] Univ Minnesota, 500 Pillsbury Dr SE, Minneapolis, MN 55455 USA
来源
NETWORKS & SPATIAL ECONOMICS | 2019年 / 19卷 / 01期
基金
美国国家科学基金会;
关键词
Autonomous vehicles; Trajectory reservation; Dynamic traffic assignment; Cell transmission model; CELL TRANSMISSION MODEL; SYSTEM; PERMITS; MANAGEMENT; WAVES;
D O I
10.1007/s11067-018-9422-1
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
We present a combinatorial assignment algorithm for reserving space-time trajectories from origins to destinations given an ordered list of vehicles. Space-time trajectories include guaranteed arrival times at every node in the path, including at the destination. Traffic flows are modeled using the cell transmission model, a Godunov approximation to the kinematic wave model. Space-time trajectories are constructed to follow the cell transmission model constraints and first-in-first-out behavior. Reservation-based intersection control for connected autonomous vehicles, which determines intersection access and delays for individual vehicles, is used to ensure that reserved trajectories are followed. The algorithm is suitable for city networks. Results show that vehicles with higher priority tend to have much lower travel times. In addition, the trajectory reservation system reduced overall congestion in the network compared with dynamic user equilibrium assignments.
引用
收藏
页码:27 / 55
页数:29
相关论文
共 50 条
  • [31] The Performance of Connected and Autonomous Vehicles with Trajectory Planning in a Fixed Signal Controlled Intersection
    Liu, Shaojie
    Fan, Wei
    Jiao, Shuaiyang
    Li, Aizeng
    [J]. PROMET-TRAFFIC & TRANSPORTATION, 2024, 36 (01): : 164 - 176
  • [32] Multi-Agent Probabilistic Ensembles With Trajectory Sampling for Connected Autonomous Vehicles
    Wen, Ruoqi
    Huang, Jiahao
    Li, Rongpeng
    Ding, Guoru
    Zhao, Zhifeng
    [J]. IEEE Transactions on Vehicular Technology, 2024, 73 (11) : 16076 - 16091
  • [33] A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network
    Ngoduy, Dong
    Nguyen, Cuong H. P.
    Lee, Seunghyeon
    Zheng, Zuduo
    Lo, Hong K.
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2024, 186
  • [34] Dynamic Rearrangement Compression Algorithm for Intelligent Connected Vehicles
    Wu, Yujing
    Li, Jinze
    Xu, Yinan
    Chung, Jin-Gyun
    Dai, Yilin
    Xu, Yihu
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (10) : 10350 - 10360
  • [35] Trajectory Generation for Autonomous Vehicles
    Vu Trieu Minh
    [J]. MECHATRONICS 2013: RECENT TECHNOLOGICAL AND SCIENTIFIC ADVANCES, 2014, : 615 - 626
  • [36] Trajectory tracking algorithm for autonomous vehicles using adaptive reinforcement learning
    De Paula, Mariano
    Acosta, Gerardo G.
    [J]. OCEANS 2015 - MTS/IEEE WASHINGTON, 2015,
  • [37] MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles
    Gong, Yue-Jiao
    Huang, Ting
    Ma, Yi-Ning
    Jeon, Sang-Woon
    Zhang, Jun
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (04) : 3714 - 3727
  • [38] Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm
    Valera, Angel
    Valero, Francisco
    Valles, Marina
    Besa, Antonio
    Mata, Vicente
    Llopis-Albert, Carlos
    [J]. SUSTAINABILITY, 2021, 13 (03) : 1 - 23
  • [39] Automated vehicles: autonomous or connected?
    Parent, Michel
    [J]. 2013 IEEE 14TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2013), VOL 1, 2013, : 2 - 2
  • [40] Trajectory Planning for Autonomous Vehicles at Autonomous Intersection
    Chen, Jing
    Mu, Chen
    Zhao, Lu
    [J]. CICTP 2020: ADVANCED TRANSPORTATION TECHNOLOGIES AND DEVELOPMENT-ENHANCING CONNECTIONS, 2020, : 332 - 342