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Research on run-time risk evaluation method based on operating scenario data for autonomous train.

Ru Niu1, Sifan You2

  • 1State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China.

Accident; Analysis and Prevention
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for real-time risk assessment in unmanned trains by monitoring operational design domain parameters. It enhances train safety decision-making through fuzzy dynamic reasoning and Bayesian networks.

Keywords:
Autonomous TrainDynamic Bayesian Network (DBN)Fuzzy Set TheoryOperational Design Domain (ODD)Run-time Risk Evaluation

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Area of Science:

  • Railway Engineering
  • Artificial Intelligence
  • Risk Management

Background:

  • Next-generation railways focus on autonomous operation, demanding robust risk situation awareness and safety decision-making.
  • Assessing train operation risks is challenging due to complex environmental factors and the need for real-time monitoring.

Observation:

  • Key scenario parameters, defined by Operational Design Domain (ODD) and operating scenarios, are crucial for train control systems.
  • Dependencies among these parameters are essential for building accurate risk assessment models.

Findings:

  • A Dynamic Bayesian Network (DBN) structure is derived from key scenario parameters and their dependencies.
  • Fuzzy Set Theory is integrated to handle data probability uncertainty, enabling fuzzy dynamic reasoning.
  • The proposed method effectively analyzes runtime risks using operational data, as demonstrated by a Singapore MTR accident case.

Implications:

  • This research contributes to data-driven runtime risk analysis for enhancing the safety of autonomous train operations.
  • The developed framework provides a foundation for more reliable risk assessment in complex railway environments.
  • Accurate real-time risk inference is vital for improving the safety and autonomy of future train systems.