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

K. B. Devika, ASISH BERA, Venkata Ramani Yellapantula, ARDHENDU BEHERA*, YONGHUAI LIU, Shankar C. Subramanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

2 Citations (Scopus)
157 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationIntelligent Transportation Systems. IEEE International Conference. 24th 2021.
Publication statusE-pub ahead of print - 25 Oct 2021


  • Deep Learning
  • Convolutional Neural Network
  • Collision Avoidance
  • Driver distraction
  • Driver Activity Recognition
  • Hardware in Loop
  • Full State Feedback Controller

Research Centres

  • Centre for Intelligent Visual Computing Research
  • Data Science STEM Research Centre


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