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Potential field reconstruction-based path planning system for autonomous vehicle with enhancing stability.

Zihao Fu¹, Zhixian Liu¹, Weihua Tan^2,3

¹Huaihua University, Huaihua, China.

|September 30, 2025

Summary

This study introduces a novel potential field reconstruction-based path planning system (PFR-BPPS) for autonomous vehicles (AV). The PFR-BPPS enhances stability and velocity adaptability during emergency scenarios.

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Path planning and tracking are crucial for autonomous vehicle (AV) operation.
Conventional methods struggle to ensure AV stability during emergency scenarios.
Existing systems often prioritize obstacle avoidance over dynamic stability in critical situations.

Purpose of the Study:

To propose a novel path planning system for autonomous vehicles (AV) that ensures stability during emergency scenarios.
To address the limitations of conventional path planning methods in maintaining AV stability under sudden events.
To enhance the velocity adaptability and path stability of AVs in critical situations.

Main Methods:

A potential field reconstruction-based path planning system (PFR-BPPS) was developed.
The system comprises a potential field reconstruction module and an adaptive fusion module.
Fuzzy inference rules were employed for adaptive integration of potential velocity and stability fields.

Main Results:

The PFR-BPPS demonstrated superior performance in simulations conducted on the Matlab-Carsim co-simulation platform.
The system effectively improved velocity adaptability for autonomous vehicles.
The proposed method significantly enhanced path stability during emergency situations.

Conclusions:

The PFR-BPPS offers a robust solution for path planning in autonomous vehicles, particularly in emergency scenarios.
The integration of potential velocity and stability fields through fuzzy logic improves overall system performance.
This approach contributes to safer and more reliable autonomous vehicle operation.