TY - JOUR
T1 - 智能车辆规划与控制策略学习方法综述
AU - Gong, Jianwei
AU - Gong, Cheng
AU - Lin, Yunlong
AU - Li, Zirui
AU - Chao, Lü
N1 - Publisher Copyright:
© 2022 Beijing Institute of Technology. All rights reserved.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - intelligent vehicles(IV)
KW - machine learning
KW - model predictive control
KW - motion planning and control
UR - http://www.scopus.com/inward/record.url?scp=85137162840&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2022.095
DO - 10.15918/j.tbit1001-0645.2022.095
M3 - 文章
AN - SCOPUS:85137162840
SN - 1001-0645
VL - 42
SP - 665
EP - 674
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 7
ER -