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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/8491
Title: Driver Distraction Recognition-driven Collision Avoidance Algorithm for Active Vehicle Safety
Authors: Bera, Asish
Keywords: Computer Science
Collision Avoidance
Convolutional neural network (CNN)
Driver Distraction
Driver Activity Recognition
Hardware in Loop
Issue Date: 2021
Publisher: IEEE
Abstract: This 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.
URI: https://ieeexplore.ieee.org/abstract/document/9564648
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8491
Appears in Collections:Department of Computer Science and Information Systems

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