A feasibility study of train automatic stop control using range sensors

被引:10
作者
Yoshimoto, K
Kataoka, K
Komaya, K
机构
来源
2001 IEEE INTELLIGENT TRANSPORTATION SYSTEMS - PROCEEDINGS | 2001年
关键词
train control; ATO; TASC; range sensor;
D O I
10.1109/ITSC.2001.948763
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Automatic train control plays a key role in improving the efficiency and safety of train movements, as well as the riding comfort of passengers. In Japan, train control systems have been successfully implemented since 1980s. These systems have been required to obtain train position and speed as accurately as possible. This has mostly depended on axle generators and transponders. More specifically, the axle generators measure the speed and the moving distance from the reference points specified by the transponders. However, train control systems using these devices still fail to get correct train position, due to skidding or slipping, until passing over reference points. This paper focuses on train automatic stop control (TASC), and presents a new TASC system using a commercial range sensor instead of transponders so that a train implementing the system can detect its position continuously.
引用
收藏
页码:802 / 807
页数:6
相关论文
共 26 条
  • [21] Viewpoint Planning for Range Sensors Using Feature Cluster Constrained Spaces for Robot Vision Systems
    Magana, Alejandro
    Vlaeyen, Michiel
    Haitjema, Han
    Bauer, Philipp
    Schmucker, Benedikt
    Reinhart, Gunther
    SENSORS, 2023, 23 (18)
  • [22] Study on multi-objective train control based on hybrid particle swarm optimization
    Yu J.
    He Z.-Y.
    Qian Q.-Q.
    Tiedao Xuebao/Journal of the China Railway Society, 2010, 32 (01): : 38 - 42
  • [23] Communication-Based Train Control System Performance Optimization Using Deep Reinforcement Learning
    Zhu, Li
    He, Ying
    Yu, F. Richard
    Ning, Bin
    Tang, Tao
    Zhao, Nan
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (12) : 10705 - 10717
  • [24] Evaluating Influence Factors of Energy Consumption for Urban Rail Timetable Using an Optimized Train Control Method
    Liu, Jiang
    Zhang, Jiao
    Xie, Hao
    Xiao, Si-yu
    2018 4TH INTERNATIONAL CONFERENCE ON GREEN MATERIALS AND ENVIRONMENTAL ENGINEERING (GMEE 2018), 2018,
  • [25] Study on Energy-Saving Optimization of Train Coasting Control Based on Multi-Population Genetic Algorithm
    Lin, Chao
    Fang, Xingqi
    Zhao, Xia
    Zhang, Qiongyan
    Liu, Xun
    2017 3RD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2017, : 627 - 632
  • [26] Virtual balise placement for GNSS-based train control using aquila optimization-enhanced multi-objective optimization
    Wang, Si-Qi
    Liu, Jiang
    Cai, Bai-Gen
    Wang, Jian
    Lu, De-Biao
    EXPERT SYSTEMS WITH APPLICATIONS, 2025, 273