SIA OpenIR  > 工业控制网络与系统研究室
Robust Vehicle and Surrounding Environment Dynamic Analysis for Assistive Driving Using Visual-Inertial Measurements
Zhang YL(张吟龙)1,2,3; Liang W(梁炜)1,2; He, Hongsheng4; Tan JD(谈金东)5
Department工业控制网络与系统研究室
Source PublicationIEEE Access
ISSN2169-3536
2019
Volume7Pages:8002-8017
Indexed BySCI ; EI
EI Accession number20185206300233
WOS IDWOS:000456912900001
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China ; International Partnership Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
KeywordVehicle and Surrounding Environment Dynamic Analysis (VSEDA) Assistive Driving Monocular Camera, Inertial Measurement Unit (IMU) Multi-sensor Fusion, Complex Road Conditions
AbstractVehicle and surrounding environment dynamic analysis (VSEDA) is an indispensable component of modern assistive drivings. A robust and accurate VSEDA could ensure the driving system reliability in presence of highly dynamic environments. This paper proposes a novel VSEDA framework by fusing the measurements from an inertial sensor and a monocular camera. Compared to traditional visual-inertial based assistive driving methods, the proposed approach can analyze both the vehicle dynamics and the surrounding environment. Even in the scenario that moving objects occupy a majority area of the scene captured in the image, the proposed method can still robustly analyze the surrounding environment by identifying the static inliers and dynamic inliers, which lie on stationary objects and moving objects, respectively. The theoretical framework consists of three steps. Firstly, the vehicle nonholonomic constraint is applied to pairwise feature matching. For vehicle dynamic analysis, the static inliers are selected by choosing the features with their histogram bins consistent with inertial orientations. Secondly, for the surrounding environment dynamic analysis, the dynamic inliers are matched through histogram voting, together with the developed part-based vehicle detection model that can segment and match the vehicle regions from the background in image pairs. Finally, both the vehicle dynamics and surrounding environments are analyzed with static and dynamic inliers respectively. The proposed method has been evaluated on the challenging datasets, part of which were collected during rush hours in downtown areas. The experimental results prove the effectiveness and accuracy of the proposed VSEDA.
Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordVISION ; MOTION ; SYSTEM ; MODEL ; ROAD
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Natural Science Foundation of China[61772351] ; National Natural Science Foundation of China[71661147005] ; National Natural Science Foundation of China[61673371] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; National Key Research and Development Program of China[2017YFE0101200] ; National Key Research and Development Program of China[2017YFE0101200] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; National Natural Science Foundation of China[61673371] ; National Natural Science Foundation of China[71661147005] ; National Natural Science Foundation of China[61772351]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23944
Collection工业控制网络与系统研究室
Corresponding AuthorLiang W(梁炜); Tan JD(谈金东)
Affiliation1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China
3.University of Chinese Academy of Sciences, Beijing, 100049, China.
4.Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, Kansas, 67260, USA
5.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA
Recommended Citation
GB/T 7714
Zhang YL,Liang W,He, Hongsheng,et al. Robust Vehicle and Surrounding Environment Dynamic Analysis for Assistive Driving Using Visual-Inertial Measurements[J]. IEEE Access,2019,7:8002-8017.
APA Zhang YL,Liang W,He, Hongsheng,&Tan JD.(2019).Robust Vehicle and Surrounding Environment Dynamic Analysis for Assistive Driving Using Visual-Inertial Measurements.IEEE Access,7,8002-8017.
MLA Zhang YL,et al."Robust Vehicle and Surrounding Environment Dynamic Analysis for Assistive Driving Using Visual-Inertial Measurements".IEEE Access 7(2019):8002-8017.
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