Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives-Reference-Cited by-同舟云学术

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Published:2023-06 Issue:6 Volume:8 Page:3692-3711
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Teng Siyu¹^ORCID,Hu Xuemin²^ORCID,Deng Peng²,Li Bai³^ORCID,Li Yuchen¹^ORCID,Ai Yunfeng⁴^ORCID,Yang Dongsheng⁵^ORCID,Li Lingxi⁶^ORCID,Xuanyuan Zhe⁷^ORCID,Zhu Fenghua⁸^ORCID,Chen Long⁸^ORCID

Affiliation:

1. BNU-HKBU United International College, Zhuhai, China

2. School of Computer Science and Information Engineering, Hubei University, Wuhan, China

3. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Changsha

4. University of Chinese Academy of Sciences, Beijing, China

5. School of Public Management/Emergency Management, Jinan University, Guangzhou, China

6. Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

7. Guangdong Provincial Key Lab of IRADS, BNU-HKBU United International College, Zhuhai, China

8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province in China

2022 Opening Foundation of State Key Laboratory of Management and Control for Complex Systems

Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science

BNU-HKBU United International College

Guangdong Higher Education Upgrading Plan with UIC Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/10187697/10122127.pdf?arnumber=10122127

Reference168 articles.

1. Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

2. Nonlinear inverse reinforcement learning with Gaussian processes;levine;Proc Adv Neural Inf Process Syst,0

3. Maximum entropy inverse reinforcement learning;ziebart;Proc AAAI Conf Artif Intell,0

4. Learning Reward Functions by Integrating Human Demonstrations and Preferences

5. Mapless Navigation With Safety-Enhanced Imitation Learning

Cited by 57 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Geometric field model of driver’s perceived risk for safe and human-like trajectory planning;Transportation Research Part C: Emerging Technologies;2024-02

2. Introductory Chapter: MOTION PLANNING FOR DYNAMIC AGENTS;Motion Planning for Dynamic Agents;2024-01-17

3. Safety analysis of autonomous vehicles based on target detection error;IET Intelligent Transport Systems;2024-01-02

4. Adaptive Output Feedback Control for Uncertain Nonlinear Systems Subject to Deferred State Constraints;IEEE Access;2024

5. Research on Road Object Detection Model Based on YOLOv4 of Autonomous Vehicle;IEEE Access;2024