Action-Conditioned Traffic Scene Prediction for Interactive Planning-Reference-Cited by-同舟云学术

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Action-Conditioned Traffic Scene Prediction for Interactive Planning

Published:2022-02-06 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Electronics, Information, and Communication (ICEIC)
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Author:

Kim Chan¹,Cho Jae-Kyung¹,Jung Younghwa¹,Seo Seung-Woo¹,Kim Seong-Woo¹

Affiliation:

1. Seoul National University,Seoul,South Korea

Funder

National Research Foundation of Korea

Seoul National University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9748174/9748152/09748470.pdf?arnumber=9748470

Reference19 articles.

1. Social LSTM: Human Trajectory Prediction in Crowded Spaces

2. Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

3. Exploiting map information for driver intention estimation at road intersections

4. Sequence-to-sequence prediction of vehicle trajectory via lstm encoderdecoder architecture;park;2018 IEEE Intelligent Vehicles Symposium (IV),0

5. Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Cited by 1 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. Towards a Benchmark for Trajectory Prediction of Autonomous Vehicles;2023 10th International Conference on Dependable Systems and Their Applications (DSA);2023-08-10

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