Research on Key Technologies and Development Trends in Intelligent Vehicle Path Planning
DOI:
https://doi.org/10.54691/pfz3ag43Keywords:
Intelligent vehicles, path planning, RRT algorithm, neural networks, traffic efficiency.Abstract
With the rapid development of intelligent transportation systems (ITS), intelligent vehicle path planning technology has become a key component in enhancing traffic efficiency, reducing energy consumption, and improving road safety. This paper reviews the latest research progress in the field of intelligent vehicle path planning both domestically and internationally, with a focus on analyzing optimization methods of mainstream algorithms and their application effects under different scenarios. Through an in-depth analysis of the Rapidly-exploring Random Tree (RRT) algorithm and its improved versions (such as RRT*, HC-RRT), critical technologies such as node selection optimization and path smoothing are summarized. Meanwhile, this paper explores innovative applications of neural networks and reinforcement learning in path planning. By combining experimental data and case studies, various algorithms' performance in complex environments is evaluated, and a multi-dimensional evaluation index system is constructed. The results indicate that intelligent path planning technology significantly enhances travel efficiency and reduces energy consumption, providing theoretical support and practical guidance for the sustainable development of future ITS. This study not only offers systematic theoretical references for researchers in related fields but also proposes feasible suggestions for optimizing industrial applications.
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