Integrating Graph Search, Sampling, and Neural Networks for Optimized Vehicle Path Planning

Xinze Li

2025

Abstract

With the development of autonomous driving technology, path planning has become one of the core issues, aiming to ensure the safety and efficiency of vehicles in complex and dynamic environments. However, traditional path planning methods, especially graph-based algorithms, have limitations when facing changing traffic and environmental factors. Therefore, it is particularly important to find more efficient and adaptive path-planning strategies. In recent years, deep reinforcement learning (DRL) has provided new solutions for path planning and promoted the advancement of related technologies. The theme of this paper is to review the research progress of path planning for autonomous driving vehicles, focusing on the evolution from traditional graph algorithms to modern deep learning methods. This paper will review from the following perspectives: first, discuss traditional path planning methods and their limitations; second, analyze the application and advantages of deep reinforcement learning in path planning; finally, explore the latest research progress of combining deep learning with traditional path planning methods. In addition, this paper will summarize the shortcomings of current research and look forward to the direction of future development.

Paper Citation

in Harvard Style

Li X. (2025). Integrating Graph Search, Sampling, and Neural Networks for Optimized Vehicle Path Planning. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 52-59. DOI: 10.5220/0013678000004670

in Bibtex Style

@conference{icdse25,
author={Xinze Li},
title={Integrating Graph Search, Sampling, and Neural Networks for Optimized Vehicle Path Planning},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={52-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013678000004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Integrating Graph Search, Sampling, and Neural Networks for Optimized Vehicle Path Planning
SN - 978-989-758-765-8
AU - Li X.
PY - 2025
SP - 52
EP - 59
DO - 10.5220/0013678000004670
PB - SciTePress