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Fuzzy Petri Nets for Traffic Node Reliability.

Gabor Kiss1, Peter Bakucz1

  • 1Institute of Safety Science and Cybersecurity, Obuda University, 1034 Budapest, Hungary.

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|October 16, 2024
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Summary
This summary is machine-generated.

Fuzzy Petri nets offer a solution for self-driving cars by managing complex traffic data and ensuring node reliability. This approach enhances perception systems and validates autonomous vehicle safety.

Keywords:
Petri netsautonomous vehiclesfuzzy analysisminimum cut setreal measured database

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Area of Science:

  • Robotics and Artificial Intelligence
  • Control Systems Engineering
  • Transportation Engineering

Background:

  • Self-driving cars generate massive sensor data, posing challenges for traditional traffic management and validation.
  • Current methods struggle with the complexity of interpreting numerous traffic junctions, impacting autonomous vehicle feasibility.
  • The reliability of perception systems is critical for the safe operation of self-driving cars.

Purpose of the Study:

  • To introduce Fuzzy Petri nets as a novel solution for managing large-scale traffic data in self-driving cars.
  • To analyze traffic node safety and reliability using Petri nets and fuzzy logic.
  • To demonstrate how Fuzzy Petri nets can improve the efficiency of deep learning perception models.

Main Methods:

  • Utilizing modified Fuzzy Petri net procedures to model and analyze traffic node dynamics.
  • Applying fuzzy analysis and Petri nets to assess the reliability of traffic nodes.
  • Leveraging real traffic databases to inform the fuzzy extension of Petri nets.

Main Results:

  • Fuzzy Petri nets provide a manageable model for vast amounts of traffic data.
  • The method accurately describes node reliability through its dynamics, crucial for perception.
  • A smaller deep learning mesh is required when node reliability is accurately determined.

Conclusions:

  • Fuzzy Petri nets present a viable and economical solution for self-driving car data management and validation.
  • The developed approach enhances the safety analysis of traffic nodes for autonomous systems.
  • This research contributes to the advancement of reliable perception models in self-driving technology.