Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception-Reference-Cited by-同舟云学术

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Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception

Published:2023 Issue: Volume: Page:1-15
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Li Dachuan¹^ORCID,Liu Bowen²,Huang Zijian²,Hao Qi¹,Zhao Dezong³^ORCID,Tian Bin⁴

Affiliation:

1. Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China

2. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

3. James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom

4. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China

Funder

National Natural Science Foundation of China

Science and Technology Innovation Committee of Shenzhen City

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/7448921/10195962.pdf?arnumber=10195962

Cited by 4 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. RCAFusion: Cross Rubik Cube Attention Network for Multi-modal Image Fusion of Intelligent Vehicles;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. Hierarchical Uncertainty-aware Autonomous Driving in Lane-changing Scenarios: Behavior Prediction and Motion Planning;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

3. 4D mmWave Radar for Autonomous Driving Perception: A Comprehensive Survey;IEEE Transactions on Intelligent Vehicles;2024-04

4. A Lifetime Management Framework for SOTIF-Oriented Vehicle Dynamics Control: A Data-driven Approach;IEEE Transactions on Intelligent Vehicles;2023

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