Learning Driving Models From Parallel End-to-End Driving Data Set
文献类型：期刊论文
| 作者 | Chen, Long1,4 ; Wang, Qing1,4; Lu, Xiankai5; Cao, Dongpu1,3; Wang, Fei-Yue2
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| 刊名 | PROCEEDINGS OF THE IEEE
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| 出版日期 | 2020-02-01 |
| 卷号 | 108期号:2页码:262-273 |
| ISSN号 | 0018-9219 |
| 关键词 | Data models Training Adaptation models Task analysis Reinforcement learning Decision making Transforms Data set end-to-end driving parallel driving |
| DOI | 10.1109/JPROC.2019.2952735 |
| 通讯作者 | Wang, Fei-Yue(feiyue@ieee.org) |
| 英文摘要 | Parallel end-to-end driving aims to improve the performance of end-to-end driving models using both simulated- and real-world data. However, how to efficiently utilize the data from both the simulated world and the real world remains a difficult issue, since these data are usually not well aligned. In this article, we build a data set called the parallel end-to-end driving data set (PED) for parallel end-to-end driving research. PED consists of 13 000 images from the simulated world and 13 000 images from the real world that are used to train the model, as well as 2700 images from the real world that are used to test the model. The simulated-world data in PED are constructed according to the real world, and each simulated-world image corresponds to a real-world image. PED also contains the vehicle measurement data (GPS, speed, steering angle, and heading direction of the vehicle) related to both the simulated- and real-world images, which are not available in some other data sets. We conduct two types of experiments to illustrate the effectiveness and the superiority of PED and explore some ways to mix the simulated-world data with the real-world data to improve the performance of end-to-end driving models. |
| 资助项目 | Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; National Key Research and Development Program of China[2018YFB1305002] ; National Natural Science Foundation of China[61790565] ; National Natural Science Foundation of China[61773414] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000510677500005 |
| 资助机构 | Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28618]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Fei-Yue |
| 作者单位 | 1.VIPioneers HuiTuo Inc, Qingdao 266109, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada 4.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China 5.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates |
| 推荐引用方式 GB/T 7714 | Chen, Long,Wang, Qing,Lu, Xiankai,et al. Learning Driving Models From Parallel End-to-End Driving Data Set[J]. PROCEEDINGS OF THE IEEE,2020,108(2):262-273. |
| APA | Chen, Long,Wang, Qing,Lu, Xiankai,Cao, Dongpu,&Wang, Fei-Yue.(2020).Learning Driving Models From Parallel End-to-End Driving Data Set.PROCEEDINGS OF THE IEEE,108(2),262-273. |
| MLA | Chen, Long,et al."Learning Driving Models From Parallel End-to-End Driving Data Set".PROCEEDINGS OF THE IEEE 108.2(2020):262-273. |
入库方式： OAI收割
来源：自动化研究所
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