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Game-Theoretic Motion Planning with Perception Uncertainty and Right-of-Way Constraints.

Pouya Panahandeh1, Ahmad Reza Alghooneh1, Mohammad Pirani2

  • 1Mechanical and Mechatronics Engineering Department, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada.

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Summary
This summary is machine-generated.

This study enhances autonomous vehicle (AV) motion planning by using game theory to ensure right-of-way adherence and manage sensor uncertainties. The approach improves AV reliability and adaptability in complex traffic scenarios.

Keywords:
autonomous vehiclegame theorymotion planningsensor fusionuncertainty

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Game Theory

Background:

  • Autonomous vehicle (AV) motion planning faces challenges in adhering to traffic rules like right-of-way.
  • Handling uncertainties inherent in sensor data and other road users' behavior is critical for safe AV operation.

Purpose of the Study:

  • To develop a robust motion planning framework for AVs that addresses right-of-way adherence and uncertainty.
  • To leverage game-theoretic approaches for strategic decision-making in dynamic traffic environments.

Main Methods:

  • Utilized Stackelberg and Nash Bayesian (Bayesian) game-theoretic frameworks to model interactions between road users.
  • Modeled road user interactions as a hierarchical relationship for strategic trajectory optimization.
  • Incorporated probabilistic beliefs and sensor measurement updates within the Bayesian equilibrium for uncertainty handling.

Main Results:

  • The proposed approach enables AVs to strategically optimize trajectories while considering other road users' actions and priorities.
  • The Bayesian equilibrium aspect allows for informed decision-making under sensory uncertainty.
  • Experimental assessments confirmed the approach's effectiveness in improving AV motion planning reliability and adaptability.

Conclusions:

  • The game-theoretic framework effectively addresses key challenges in AV motion planning, specifically right-of-way adherence and uncertainty.
  • The developed method enhances the robustness and adaptability of autonomous driving systems.
  • This research contributes to safer and more efficient autonomous vehicle navigation.