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dc.contributor.authorZhang, Yinlong
dc.contributor.authorLiang, Wei
dc.contributor.authorHe, Hongsheng
dc.contributor.authorTan, Jindong
dc.date.accessioned2019-01-12T04:40:47Z
dc.date.available2019-01-12T04:40:47Z
dc.date.issued2018-12-24
dc.identifier.citationY. Zhang, W. Liang, H. He and J. Tan, "Robust Vehicle and Surrounding Environment Dynamic Analysis for Assistive Driving Using Visual-Inertial Measurements," in IEEE Accessen_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2018.2889320
dc.identifier.urihttp://hdl.handle.net/10057/15746
dc.descriptionPublished in IEEEAccess, Multidisciplinary, Rapid review, Open access journalen_US
dc.description.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.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Access;
dc.subjectVehicle and Surrounding Environment Dynamic Analysis (VSEDA)en_US
dc.subjectAssistive drivingen_US
dc.subjectMonocular cameraen_US
dc.subjectInertial Measurement Unit (IMU)en_US
dc.subjectMulti-sensor fusionen_US
dc.subjectComplex road conditionsen_US
dc.titleRobust vehicle and surrounding environment dynamic analysis for assistive driving using visual-inertial measurementsen_US
dc.typeArticleen_US
dc.rights.holderOpen Accessen_US


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