Driver Distraction Recognition-driven Collision Avoidance Algorithm for Active Vehicle Safety

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...

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Vydáno v:	2021 IEEE International Intelligent Transportation Systems Conference (ITSC) s. 237 - 243
Hlavní autoři:	Devika, K. B., Bera, Asish, Yellapantula, Venkata Ramani Shreya, Behera, Ardhendu, Liu, Yonghuai, Subramanian, Shankar C.
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 19.09.2021
Témata:	Collision avoidance Convolutional Neural Network Driver Activity Recognition Driver Distraction Full State Feedback Controller Hardware in Loop Heuristic algorithms Roads Safety Software Software algorithms State feedback
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Popis
Shrnutí:	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.
DOI:	10.1109/ITSC48978.2021.9564648