license: mit
task_categories:
- robotics
language:
- en
tags:
- AI
- ICRA
ICRA 2021 Accepted Paper Meta Info Dataset
This dataset is collect from the ICRA 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021 accepted papers' meta info (https://ieeexplore.ieee.org/xpl/conhome/9560720/proceeding) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/icra2021). For researchers who are interested in doing analysis of ICRA 2021 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the ICRA 2021 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Online Prediction of Lane Change with a Hierarchical Learning-Based Approach",
"detail_url": "https://ieeexplore.ieee.org/document/9812269/",
"author_list": ["Xishun Liao", "Ziran Wang", "Xuanpeng Zhao", "Zhouqiao Zhao", "Kyungtae Han", "Prashant Tiwari", "Matthew J. Barth", "Guoyuan Wu", "Xishun Liao", "Ziran Wang", "Xuanpeng Zhao", "Zhouqiao Zhao", "Kyungtae Han", "Prashant Tiwari", "Matthew J. Barth", "Guoyuan Wu"],
"abstract": "In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven vehicles will share the road networks together. In such a mixed traffic environment, CAVs need to understand and predict maneuvers of surrounding vehicles for safer and more efficient interactions, especially when human drivers bring in a wide range of uncertainties. In this paper, we propose a learning-based lane..."
}
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