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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/8491
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dc.contributor.authorBera, Asish-
dc.date.accessioned2023-01-16T06:31:25Z-
dc.date.available2023-01-16T06:31:25Z-
dc.date.issued2021-
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/9564648-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8491-
dc.description.abstractThis paper integrates human driver factors with a model-based Collision Avoidance System (CAS) to enhance the safety of semi-autonomous vehicles. Driver Activity Recognition (DAR) through Driver Distraction States (DDS) has been used as the key component to trigger the CAS so that collisions can be averted. DDS has been generated using realistic normal driving scenarios and suitably integrated with a Full State Feedback (FSF) controller-based CAS. The integrated algorithm has been tested using a Hardware in Loop (HiL) setup, which is interfaced with the vehicle dynamics software IPG TruckMaker ® . The performance of the algorithm has been evaluated for various on-road scenarios and found to be effective in avoiding rear-end collisions.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectComputer Scienceen_US
dc.subjectCollision Avoidanceen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectDriver Distractionen_US
dc.subjectDriver Activity Recognitionen_US
dc.subjectHardware in Loopen_US
dc.titleDriver Distraction Recognition-driven Collision Avoidance Algorithm for Active Vehicle Safetyen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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