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Expert experience-guided virtual datasets for adaptive automatic driving in metro trains.

Yunhu Huang1, Wendi Zhao2, Dewang Chen3,4

  • 1College of Computer and Data Science, Minjiang University, Fuzhou, 350108, China. yunhuhuang@aliyun.com.

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Summary

This study introduces a new framework for adaptive automatic driving (AAD) in metro trains, reducing data needs and costs. It enhances energy efficiency and passenger satisfaction for scalable AAD deployment.

Keywords:
Adaptive automatic drivingExpert experience-guided learningFuzzy multi-criteria decision makingMetro train controlVirtual datasets

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Area of Science:

  • Railway Engineering
  • Artificial Intelligence
  • Control Systems

Background:

  • Adaptive automatic driving (AAD) for metro trains faces challenges due to limited operational data and high acquisition costs.
  • Existing systems often require extensive real-world data, hindering scalable deployment, especially in low-infrastructure environments.

Purpose of the Study:

  • To propose a novel four-stage framework for scalable AAD deployment in metro trains.
  • To integrate expert operational knowledge to reduce reliance on empirical datasets.
  • To enhance energy efficiency, passenger comfort, and stopping precision in AAD systems.

Main Methods:

  • Synthesized 2.9 million high-fidelity driving curves using expert-defined motion constraints.
  • Employed a fuzzy multi-criteria decision-making system for optimal curve selection based on expert knowledge.
  • Utilized a random forest model trained on structured parametric representations for trajectory reconstruction.

Main Results:

  • Achieved a 12% reduction in energy consumption and 95% passenger satisfaction.
  • Demonstrated high precision with stopping targets of [Formula: see text] m and arrival time targets of [Formula: see text] s.
  • Obtained 98.2% reconstruction accuracy and a 76% reduction in data costs.

Conclusions:

  • The proposed framework effectively bridges expert experience and data-driven learning for robust AAD.
  • It enables scalable AAD deployment with minimal data dependency and reduced costs.
  • The framework is compatible with existing automatic train operation (ATO) systems.