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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

Summary

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 driving cooperative automated driving infrastructure-based cooperative driving path planning potential 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.