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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Vydané v:2021 IEEE International Intelligent Transportation Systems Conference (ITSC) s. 237 - 243
Hlavní autori: Devika, K. B., Bera, Asish, Yellapantula, Venkata Ramani Shreya, Behera, Ardhendu, Liu, Yonghuai, Subramanian, Shankar C.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 19.09.2021
Predmet:
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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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