Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (3): 46-57.doi: 10.6040/j.issn.1672-3961.0.2024.003

• Transportation Engineering—Special Issue for Intelligent Transportation • Previous Articles    

Adynamic safe elliptical path planning method for intelligent vehicles based on improved artificial potential field

ZHAO Hongzhuan1,2, ZHANG Xin1, ZHANG Beiling1, ZHAN Xin2, LI Wenyong1, YUAN Quan3, WANG Tao1, ZHOU Dan1   

  1. ZHAO Hongzhuan1, 2, ZHANG Xin1, ZHANG Beiling1, ZHAN Xin2, LI Wenyong1, YUAN Quan3, WANG Tao1, ZHOU Dan1(1. Guangxi Key Laboratory of Intelligent Transportation(Guilin University of Electronic Technology), Guilin 541004, Guangxi, China;
    2. Commercial Vehicle Technology Center, Dongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, Guangxi, China;
    3. School of Vehicles and Mobility, Tsinghua University, Beijing 100084, China
  • Published:2025-06-05

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Abstract: This study addressed the challenges inherent in the traditional artificial potential field method used in intelligent vehicle path planning, specifically the intricate calibration of the repulsive force parameter, prevalence of local extreme values, imbalance of potential field force, and the issue of unreachable targets. A novel approach was proposed: a dynamic safe elliptical path planning method for intelligent vehicles based on the improved artificial potential field. This study delineated the concept of a transverse virtual safe space, taking into account the interplay between speed and tracking error. It developed an elliptic dynamic safe distance model, both transverse and longitudinal, predicated on dynamic compensation. This model facilitated the calibration of the gravitational repulsive force range, thereby addressing the prevalent challenge of repulsive parameter calibration. Building upon this framework, the paper introduced an equilibrium factor and a variable adjustment factor, both derived from the Gaussian function. These factors were designed considering the relationship between position vectors, effectively addressing issues such as local extremes, potential field force imbalances, and target inaccessibility. A model predictive controller was meticulously designed for the effective tracking control of this path. The experimental outcomes demonstrated the efficacy of the proposed method in resolving the intricate challenges associated with the calibration of repulsive force parameters, local extremes, potential field force imbalances, and target unreachability in intelligent vehicle path planning. Notably, the operational efficiency of the algorithm was enhanced by 69.5% relative to the RRT* algorithm, and there was a notable 62.2% reduction in the average path curvature. The method significantly enhanced the smoothness and comfort in both single-obstacle and multi-obstacle vehicular simulation planning scenarios, demonstrating its versatility and effectiveness in varying conditions. The real-vehicle experiment results affirmed the applicability of the algorithm in actual path planning scenarios.

Key words: path planning, improved artificial potential field method, position vectors, dynamic compensation, model predictive control

CLC Number: 

  • U491
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