Abstract
Driver distraction is the primary cause of car accidents among USA teenage drivers. Predicting distractive driver behaviour and adapting the car systems accordingly is one solution to this problem. We use neural networks to find a correlation between driving patterns and car system variables. We conducted an experiment to induce distractive tasks to drivers and collected corresponding data patterns, then used them to train the network. With our triangulation algorithm, we reused the trained network to predict driver behaviour using the data patterns from part 1. Our neural network accurately predicts driver distraction when fed with system variables alone.
Original language | English (US) |
---|---|
Title of host publication | Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 |
Editors | Fernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 811-816 |
Number of pages | 6 |
ISBN (Electronic) | 9781538626528 |
DOIs | |
State | Published - Dec 4 2018 |
Event | 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States Duration: Dec 14 2017 → Dec 16 2017 |
Other
Other | 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 |
---|---|
Country/Territory | United States |
City | Las Vegas |
Period | 12/14/17 → 12/16/17 |
Keywords
- driver distraction
- Human- car-interaction
- Neural Network
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Safety, Risk, Reliability and Quality