交通运输系统工程与信息

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (4): 129-136.DOI: 10.16097/j.cnki.1009-6744.2022.04.015

• 系统工程理论与方法 • 上一篇    下一篇

基于复杂网络的列车辅助驾驶危险致因传播模型

张仕杰1a，唐涛* 1a，刘金涛1b，李辰岭2   

  1. 1. 北京交通大学，a. 轨道交通控制与安全国家重点实验室，b. 国家轨道交通安全评估研究中心，北京 100044； 2. 华威大学，制造工程学院，考文垂 CV4 7AL，英国
  • 收稿日期:2022-04-04 修回日期:2022-05-10 接受日期:2022-05-26 出版日期:2022-08-25 发布日期:2022-08-23
  • 作者简介:张仕杰(1992- )，男，山东荣成人，博士生。
  • 基金资助:
    中央高校基本科研业务费

Propagation Model for Hazard Causes of Intelligent Driving Assistance System Based on Complex Network

ZHANG Shi-jie1a , TANG Tao* 1a , LIU Jin-tao1b , LI Chen-ling2   

  1. 1a. State Key Laboratory of Rail Traffic Control and Safety, 1b. National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044, China; 2. Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK
  • Received:2022-04-04 Revised:2022-05-10 Accepted:2022-05-26 Online:2022-08-25 Published:2022-08-23
  • Supported by:
    Fundamental Research Funds for the Central Universities (2021QY007）

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摘要： 为了解预期功能安全(SOTIF)相关危险致因在基于智能感知的列车辅助驾驶系统 (IATDAS)中的传播特性，提升针对该类系统的危险控制能力，本文提出基于复杂网络的IATDAS 系统危险致因传播模型。该模型在SOTIF危险致因网络的基础上，提供了全局容量-负载传播机制，能有效刻画IATDAS系统的危险致因传播机制。案例分析结果表明：本文所提模型能够解决复杂致因关系下既有模型与系统实际情况不符的问题，如对于具有较长后续传播路径的致因，本文模型能够刻画其较难导致危险的实际特征；依据本文模型实施传播控制，可以显著降低危险致因的传播速度，如对影响节点范围大、前期影响节点数量增加快的危险因素进行控制时，可使其平均传播速度降低68%，比随机控制策略多降58%。该模型可以为IATDAS系统的SOTIF相关危险控制提供决策基础。

关键词: 智能交通, 致因传播模型, 复杂网络, 预期功能安全(SOTIF), 基于智能感知的列车辅助驾驶系统

Abstract: To study the safety of the intended functionality (SOTIF) of the Intelligent Awareness-based Train Driving Assistance System (IATDAS) and to improve the hazard control, this paper proposes a propagation model for hazard causes of the IATDAS based on the complex network. With the SOTIF- related hazard causes network, this model provides an overall load-capacity propagation mechanism to describe the propagation of hazard causes. The case study shows that the proposed model can overcome the deficiency of the existing models in matching the actual situation under complex causal relationships. For example, this model can represent the feature that the causes with longer subsequent propagation paths are more difficult to cause a hazard. The results also show that hazard control strategies gained from this model can significantly reduce the propagating speed. For example, in the control of causes with a large and rapid influence on the network, the average propagation speed can be reduced by 68%, which is 58% lower than that of the random control strategy. Thus, our propagation model can effectively support the decision-making in hazard control.

Key words: intelligent transportation, causes propagation model, complex network, safety of the intended functionality (SOTIF), Intelligent Awareness-based Train Driving Assistance System (IATDAS)

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