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Gradient-Based Time-Sequential Potential Field Method for Path Planning in Infrastructure-Based Cooperative Driving

Jakyung Ko¹, Inchul Yang²

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

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

This summary is machine-generated.

A new Gradient-based Time-Sequential Potential Field (GTS-PF) method enhances real-time path planning for cooperative driving automation. This approach improves responsiveness to dynamic environments and reduces computation time by 81% for infrastructure-based systems.

Keywords:

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

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Area of Science:

Intelligent Transportation Systems
Autonomous Driving
Robotics Path Planning

Background:

Conventional path planning methods struggle with computational efficiency and dynamic environment responsiveness.
Infrastructure-based systems require lightweight and adaptive path generation for cooperative driving.

Purpose of the Study:

To introduce a Gradient-based Time-Sequential Potential Field (GTS-PF) method for real-time reference-path planning.
To enhance cooperative driving automation by addressing limitations in existing path planning techniques.

Main Methods:

Interpreting the prediction time horizon as sequential temporal layers for adaptive responses.
Formulating the method based on potential field principles for efficient computation.
Separating direction and speed planning with future risk evaluation for obstacle avoidance.

Main Results:

The GTS-PF method successfully generated safe and high-quality paths in simulations.
Demonstrated significant computational efficiency, with an 81% reduction in computation time compared to baseline methods.
Validated feasibility for Roadside Unit (RSU)-level implementation in cooperative driving automation.

Conclusions:

The GTS-PF framework offers a computationally lightweight and responsive solution for real-time path generation.
The proposed method is suitable for infrastructure-led cooperative driving automation scenarios.
Enables adaptive navigation in dynamic environments, improving safety and efficiency.