Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving-Reference-Cited by-同舟云学术

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Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving

Published:2024-08 Issue:8 Volume:9 Page:7023-7030
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Huang Zhiyu¹^ORCID,Tang Chen²^ORCID,Lv Chen³^ORCID,Tomizuka Masayoshi⁴^ORCID,Zhan Wei⁴^ORCID

Affiliation:

1. University of California, Berkeley, CA, USA

2. Department of Computer Science, The University of Texas, Austin, TX, USA

3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore

4. Department of Mechanical Engineering, The University of California, Berkeley, CA, USA

Funder

Berkeley DeepDrive

Agency for Science, Technology and Research

MTC Individual Research

Ministry of Education

Tier 2

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7083369/10561888/10563192.pdf?arnumber=10563192

Reference33 articles.

1. A taxonomy and review of algorithms for modeling and predicting human driver behavior;Brown,2020

2. Improving Automated Driving Through POMDP Planning With Human Internal States

3. Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

4. Guiding Belief Space Planning with Learned Models for Interactive Merging

5. BetaZero: Belief-state planning for long-horizon POMDPs using learned approximations;Moss,2023

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