Graph Representation of Road and Traffic for Autonomous Driving

被引：2
|
作者：
Qiao, Jianglin [1 ]
Zhang, Dongmo [1 ]
de Jonge, Dave [1 ,2 ]
机构：
[1] Univ Western Sydney, Sydney, NSW, Australia
[2] CSIC, IIIA, Barcelona, Spain
来源：
PRICAI 2019: TRENDS IN ARTIFICIAL INTELLIGENCE, PT III | 2019年 / 11672卷
关键词：
D O I：
10.1007/978-3-030-29894-4_31
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Autonomous driving has the potential to radically change the way vehicles interact each other. This paper aims to develop a formal method to model high level interaction between autonomous vehicles. We introduce a concept of road graph to represent complex road situations such as intersections, road merging, unmarked roads, and traffic hazards. We then extend the concept to further represent status of vehicles, dynamics of traffic and protocols of traffic control. Specifically, we formalise two categories of traffic control protocols, time-based protocols and priority-based protocols.
引用
收藏
页码：377 / 384
页数：8
相关论文
共 50 条
  • [1] Exploring the Road Graph in Trajectory Forecasting for Autonomous Driving
    Sun, Remy
    Lingrand, Diane
    Precioso, Frederic
    [J]. 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS, ICCVW, 2023, : 71 - 80
  • [2] An obstacle representation for off-road autonomous driving
    Weber, J
    Matthies, L
    [J]. PROCEEDINGS OF THE 1996 IEEE INTELLIGENT VEHICLES SYMPOSIUM, 1996, : 345 - 350
  • [3] Road Network Graph Representation for Traffic Analysis and Routing
    Bachechi, Chiara
    Po, Laura
    [J]. ADVANCES IN DATABASES AND INFORMATION SYSTEMS, ADBIS 2022, 2022, 13389 : 75 - 89
  • [4] Road Design and Traffic Detection Methods for Autonomous Driving Scenarios
    Bai, Kang
    Fang, Xiangming
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2024,
  • [5] The Stixel world - A comprehensive representation of traffic scenes for autonomous driving
    Schneider, Lukas
    Hafner, Michael
    Franke, Uwe
    [J]. AT-AUTOMATISIERUNGSTECHNIK, 2018, 66 (09) : 745 - 751
  • [6] RIANet: Road Graph and Image Attention Network for Urban Autonomous Driving
    Ha, Timothy
    Oh, Jeongwoo
    Chung, Hojun
    Lee, Gunmin
    Oh, Songhwai
    [J]. 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, : 4805 - 4810
  • [7] Autonomous Driving Scenario Description Language Embedded with Road Network Graph Model
    Gong L.
    Sun X.-Y.
    Zhang Y.
    Zhang Y.-Y.
    Ji J.-M.
    Hua B.
    [J]. Ruan Jian Xue Bao/Journal of Software, 2023, 34 (09):
  • [8] Impact and revolution on law on road traffic safety by autonomous driving technology in China
    Xu, Lin
    He, Bingbing
    Zhou, Hanchu
    He, Jun
    [J]. COMPUTER LAW & SECURITY REVIEW, 2023, 51
  • [9] Enhanced road information representation in graph recurrent network for traffic speed prediction
    Chang, Lei
    Ma, Cheng
    Sun, Kai
    Qu, Zhijian
    Ren, Chongguang
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2023, 17 (07) : 1434 - 1453
  • [10] Trajectory-based traffic observation of cooperation at a road narrowing Implications for autonomous driving
    Quante, Laura
    Gimm, Kay
    Schiessl, Caroline
    [J]. AT-AUTOMATISIERUNGSTECHNIK, 2023, 71 (04) : 249 - 257
12345