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This study aims to improve path planning for autonomous vehicles by addressing key limitations of traditional methods.

Path planning for autonomous vehicles using an improved artificial potential field method

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This study aims to improve path planning for autonomous vehicles by addressing key limitations of traditional methods.
Introduced target distance adjustment factor to enhance repulsive potential field.
Implemented dynamic gain coefficient for the gravitational field to reduce collisions near the start point.
Adopted virtual target point mechanism to avoid local minima.
Integrated RRT algorithm to bolster dynamic programming capabilities.
Conducted MATLAB simulations to evaluate performance.
B-APF reduced average path length by 0.54% and computation time by 2.93%, while enhancing path smoothness.
Achieved a 7.43% reduction in average path length compared to the RRT algorithm.
Significantly decreased average curvature by 94.00% and maximum curvature by 80.99%.
Robustness tests confirmed an average position error of only 0.025 m under various conditions.
MPC-based tracking validation showed a lateral error of less than 0.053 m.

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The B-APF algorithm enhances path planning in autonomous vehicles, indicating improved performance in complex environments.

Li et al. (2026) studied this question.

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