Abstract Local path planning is essential for autonomous vehicles operating in complex non-convex environments. Existing geometric planners are computationally efficient but often generate curvature discontinuities and steering commands that are difficult to execute, whereas optimization-based methods usually require higher computational cost and may suffer from numerical oscillations in constrained spaces. To address these issues, this paper proposes a dynamic potential field-guided and force-coupled adaptive pure pursuit framework, termed i-APP, for real-time local path planning. The proposed method consists of three stages. First, a velocity-adaptive gradient A* search generates a collision-free initial path with speed-dependent safety margins. Second, a dynamic artificial potential field refines risky path segments through deterministic lateral offsets to improve obstacle clearance. Finally, a force-coupled adaptive pure pursuit strategy maps obstacle repulsive forces to the look-ahead target point, while forward kinematic integration and steering-rate constraints are incorporated to improve trajectory executability. Simulation and real-vehicle experiments are conducted to compare i-APP with APP, A*+APP, and EMplanner methods. The results show that i-APP reduces planning time while improving trajectory smoothness and suppressing curvature fluctuation, indicating its suitability for real-time local planning in obstacle-constrained autonomous driving scenarios. Subjects Acknowledgements This work was supported by the Shijiazhuang Science and Technology Cooperation Special Project (Grant No. SJZZXA25003) and the Tianjin Science and Technology Plan Project (Grant No. 25ZXRGGX00310). (Corresponding author: Ding Chengjun).Thanks to Chengjun Ding, Tan Zhang , Tengfei Ma , Zijian Li, Jinshen Yu, Ke Wang, Jianxin Hufor their assistance in the project. Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or