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Gradient-Based Time-Extended Potential Field Method for Real-Time Path Planning in Infrastructure-Based Cooperative

Jakyung Ko1, Inchul Yang2

  • 1Department of Civil and Environmental Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Gradient-based Time-extended Potential Field (GT-PF) for safer autonomous driving. This method generates efficient, real-time paths by analyzing road conditions and obstacles, outperforming traditional planning techniques.

Keywords:
automated drivingcooperative automated drivinginfrastructure-based cooperative drivingpath planningpotential field

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Autonomous driving systems require efficient and safe real-time path generation.
  • Existing methods like sampling-based or optimization-based planners face computational challenges.

Purpose of the Study:

  • To propose a novel real-time path generation method, Gradient-based Time-extended Potential Field (GT-PF), for cooperative autonomous driving.
  • To enhance path safety, feasibility, and computational efficiency in dynamic environments.

Main Methods:

  • Modeling the road environment and dynamic obstacles as a time-variant potential field.
  • Generating paths by tracing the negative gradient of the potential field (steepest descent).
  • Incorporating a velocity-oriented bias to satisfy kinematic constraints and desired cruising behavior.

Main Results:

  • The GT-PF approach demonstrated superior runtime efficiency and safety compared to a sampling-based Rapidly exploring Random Tree (RRT) planner.
  • Continuous trajectory generation in spatiotemporal domains was achieved with lightweight computation.
  • The method ensures generated paths meet vehicle kinematic constraints and desired speeds.

Conclusions:

  • The GT-PF method offers a computationally efficient and safe solution for real-time path generation in cooperative autonomous driving.
  • The approach is well-suited for Roadside Unit (RSU)-based infrastructure control in dynamic traffic scenarios.
  • Future research will focus on complex urban environments and multi-agent integration.