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  11. A Lyapunov Optimization-based Approach To Autonomous Vehicle Local Path Planning

A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning

Ziba Arjmandzadeh1, Mohammad Hossein Abbasi2, Hanchen Wang1

  • 1School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.

Sensors (Basel, Switzerland)
|January 8, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a new Lyapunov Optimization (LO) method for autonomous vehicle (AV) path planning. This vision-only approach significantly reduces computation time by over 20x compared to traditional methods.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous vehicles (AVs) promise enhanced safety and efficiency.
  • Achieving full autonomy (SAE Level 5) hinges on effective path planning.
  • Current path planning methods face challenges in computational complexity and safety.

Purpose of the Study:

  • To introduce a novel Lyapunov Optimization (LO) approach for local path planning in AVs.
  • To evaluate the performance of the LO method against conventional techniques.
  • To assess the feasibility of a vision-only system for AV path planning.

Main Methods:

  • A novel Lyapunov Optimization (LO) model was developed for AV local path planning.
  • The LO model was benchmarked against Model Predictive Control and a sampling-based approach.
Keywords:
Lyapunov Optimizationautonomous vehiclesmodel predictive controlpath planning

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  • An AV prototype utilized a vision-only system for object detection and data collection in Norman, Oklahoma.

    • Main Results:

    • The proposed LO strategy achieved at least a 20-fold reduction in computation time compared to baseline methods.
    • Performance was evaluated based on path smoothness, safety, and computation time.
    • The vision-only approach proved effective for real-world applicability and cost reduction.

    Conclusions:

    • Lyapunov Optimization presents a highly efficient solution for AV local path planning.
    • The vision-only implementation demonstrates practical viability for autonomous driving systems.
    • This research contributes to advancing the safety and computational efficiency of autonomous vehicles.