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.
Original language | English |
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Title of host publication | Intelligent Transportation Systems. IEEE International Conference. 24th 2021. |
Publisher | IEEE |
DOIs | |
Publication status | E-pub ahead of print - 25 Oct 2021 |
Keywords
- 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