智能车辆规划与控制策略学习方法综述

Jianwei Gong, Cheng Gong, Yunlong Lin, Zirui Li, Lü Chao

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摘要

Intelligent vehicles have achieved a considerable development in technologies and can fulfill the basic functions of autonomous driving in a limited closed environment. However, results of actual road tests show that the current technologies of intelligent vehicles still have many limitations and their large-scale application in complex urban and off-road environments still faces many challenges. As one of the key technologies, the motion planning and control technology has basically formed a complete theoretical system and has been widely applied in engineering. However, the traditional methods still have some defects in practical application, such as the inability of understanding dynamic and complex scenes, poor adaptability for different scenes, high complexity of the model, and difficulty in parameter tuning. Due to the strong ability in knowledge representation and model fitting, machine learning methods have been widely applied in perception and navigation technology for intelligent vehicles. In order to solve the problems of generalization and applicability in traditional motion planning and control techniques, many researchers have also devoted themselves to exploring the usage of deep learning, reinforcement learning, and so on machine learning methods in motion planning and control policy for intelligent vehicles. In this paper, machine learning-based methods were reviewed for motion planning and control in intelligent vehicles, analyzing the existing policy learning methods for motion planning and control from three aspects, including basic framework, basic learning paradigms, and different planning and control methods based on learning. Finally, the research status and future development directions were summarized and prospected.

投稿的翻译标题Review on Machine Learning Methods for Motion Planning and Control Policy of Intelligent Vehicles
源语言繁体中文
页(从-至)665-674
页数10
期刊Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
42
7
DOI
出版状态已出版 - 7月 2022

关键词

  • intelligent vehicles(IV)
  • machine learning
  • model predictive control
  • motion planning and control

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Gong, J., Gong, C., Lin, Y., Li, Z., & Chao, L. (2022). 智能车辆规划与控制策略学习方法综述. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 42(7), 665-674. https://doi.org/10.15918/j.tbit1001-0645.2022.095