Enhancing Closed-Loop Performance in Learning-Based Vehicle Motion Planning by Integrating Rule-Based Insights-Reference-Cited by-同舟云学术

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Enhancing Closed-Loop Performance in Learning-Based Vehicle Motion Planning by Integrating Rule-Based Insights

Published:2024-09 Issue:9 Volume:9 Page:7915-7922
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Wang Yunkai¹^ORCID,Kong Quyu²,Zhu He¹,Zhang Dongkun¹,Lin Longzhong¹^ORCID,Sha Hao¹^ORCID,Xia Xunlong²,Liang Qiao²^ORCID,Deng Bing²,Chen Ken³^ORCID,Xiong Rong¹^ORCID,Wang Yue¹^ORCID,Ye Jieping²^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

2. Alibaba Cloud, Hangzhou, China

3. Sichuan Digital Transportation Technology Company Ltd., Chendu, China

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Alibaba Group

Alibaba Innovative Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7083369/10601335/10608386.pdf?arnumber=10608386

Reference25 articles.

1. EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments

2. DriveIRL: Drive in Real Life with Inverse Reinforcement Learning

3. VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

4. Imitation Learning of Hierarchical Driving Model: From Continuous Intention to Continuous Trajectory

5. PiP: Planning-Informed Trajectory Prediction for Autonomous Driving

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