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Summary

This study introduces an executive module using hierarchical Petri nets (PNs) to coordinate autonomous car control. The RoboGraph (RG) environment facilitates PN design and execution for enhanced vehicle behavior.

Keywords:
Petri netsautonomous drivingautonomous driving behaviorsdecision-making systemvehicle control framework

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous vehicle control often relies on modular architectures with inter-process communication.
  • Existing frameworks may lack a centralized coordination mechanism for module activity.

Purpose of the Study:

  • To propose an executive module for coordinating autonomous car control systems.
  • To introduce the RoboGraph (RG) programming environment for designing and executing control behaviors.

Main Methods:

  • Utilized hierarchical interpreted binary Petri nets (PNs) to define vehicle behavior based on traffic rules.
  • Developed an executive module that commands actions and processes events via message passing.
  • Introduced RoboGraph (RG) with a graphical interface for PN development, execution, and debugging.

Main Results:

  • The executive module effectively coordinates independent control modules.
  • The RoboGraph environment provides a user-friendly platform for PN management and system monitoring.
  • The system was successfully applied to an autonomous car for elderly and disabled individuals.

Conclusions:

  • Hierarchical Petri nets offer a robust method for defining and executing complex autonomous driving behaviors.
  • The RoboGraph environment enhances the development and maintenance of autonomous vehicle control systems.
  • The proposed architecture is suitable for specialized autonomous vehicles, improving accessibility.