Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/16592
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dc.contributor.authorDesanamukula V.S.
dc.contributor.authorChilukuri P.K.
dc.contributor.authorPadala P.
dc.contributor.authorPadala P.
dc.contributor.authorPvgd P.R.
dc.date.accessioned2021-05-05T10:30:58Z-
dc.date.available2021-05-05T10:30:58Z-
dc.date.issued2020
dc.identifier.citationIEEE Access , Vol. 8 , , p. 194748 - 198778en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3033537
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16592-
dc.description.abstractThe usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-to-day human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range (1570-2100) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, Lδ, LTotal) values during benchmarking analysis on our custom-built, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.en_US
dc.titleAMMDAS: Multi-modular generative masks processing architecture with adaptive wide field-of-view modeling strategyen_US
dc.typeArticleen_US
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