CASIA OpenIR
Learning Driving Models From Parallel End-to-End Driving Data Set
Chen, Long1,2; Wang, Qing1,2; Lu, Xiankai3; Cao, Dongpu2,4; Wang, Fei-Yue5
Source PublicationPROCEEDINGS OF THE IEEE
ISSN0018-9219
2020-02-01
Volume108Issue:2Pages:262-273
Corresponding AuthorWang, Fei-Yue(feiyue@ieee.org)
AbstractParallel 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.
KeywordData models Training Adaptation models Task analysis Reinforcement learning Decision making Transforms Data set end-to-end driving parallel driving
DOI10.1109/JPROC.2019.2952735
Indexed BySCI
Language英语
Funding ProjectIntel 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] ; 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]
Funding OrganizationIntel 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
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000510677500005
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28619
Collection中国科学院自动化研究所
Corresponding AuthorWang, Fei-Yue
Affiliation1.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China
2.VIPioneers HuiTuo Inc, Qingdao 266109, Peoples R China
3.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates
4.Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Long]'s Articles
[Wang, Qing]'s Articles
[Lu, Xiankai]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Long]'s Articles
[Wang, Qing]'s Articles
[Lu, Xiankai]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Long]'s Articles
[Wang, Qing]'s Articles
[Lu, Xiankai]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.