A Review of Obstacle Avoidance Path Planning Technologies for Intelligent Vehicles

Main Article Content

Yuefan Min
School of Mathematical Sciences, Zhejiang University of Technology, Hangzhou, 310015, China

DOI: https://doi.org/10.70267/cai.26v3n3.135154

Keywords

intelligent vehicles, obstacle avoidance, environmental perception, decision-making algorithms, motion control

Abstract

As a core component of autonomous driving systems, obstacle avoidance path planning technology for intelligent vehicles aims to achieve safe and efficient navigation in dynamic environments through the coordination of multiple modules. This paper systematically reviews the current state of research on the overall architecture and key algorithms of this technology, focusing on the technical developments of the three core modules: perception, decision-making, and control. In the realm of environmental perception, technologies such as multimodal fusion, V2X collaboration, and vehicle–road–cloud integration have significantly enhanced the robustness and global awareness of perception; the path decision-making module encompasses diverse developments ranging from classical graph search algorithms to intelligent optimization methods, balancing the real-time performance and optimality of decision-making algorithms; and the motion control section summarizes the trend toward integrating deterministic methods with heuristic strategies, thereby enhancing the accuracy and adaptability of trajectory tracking. The review notes that while current technologies have progressed in modular collaboration, future breakthroughs are still needed in areas such as end-to-end learning and lightweight deployment to drive the system toward higher reliability.

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Jun 29, 2026
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Min, Y. (2026). A Review of Obstacle Avoidance Path Planning Technologies for Intelligent Vehicles. Computers and Artificial Intelligence, 3(3), 135-154. https://doi.org/10.70267/cai.26v3n3.135154