Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

By Yulan Liang, Michelle L. Reyes, John D. Lee. IEEE transactions on intelligent transportation systems (Print), 2007

489 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6fae4be58d55958c6786cc6584769a77c4faa8b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Björn Schuller
2. J. Urgen Schmidhuber

In Social Psychology

1. Björn Schuller
2. Yanchao Dong
3. Zhencheng Hu
4. Keiichi Uchimura
5. Nobuki Murayama

In Human-Computer Interaction

1. Björn Schuller
2. Yanchao Dong
3. Zhencheng Hu
4. Keiichi Uchimura
5. Nobuki Murayama
6. J. Urgen Schmidhuber

Citations in the corpus

• Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, methodology): “[51] tried to detect the driver cognitive distraction caused by interacting with in-vehicle information systems (IVISs) in real time by fusing eye movement and driving performance using an SVM.”
• Consensus-Based Distributed Support Vector Machines (2010, background): “…classification tasks emerging inappl cations such as medical imaging, bio-informatics, speech, and handwriting recognition, to name a few (Vapnik, 1998; Scḧolkopf and Smola, 2002; El-Naqa et al., 2002; Liang et al., 2007; Ganapathiraju et al., 2004; Li, 2005; Markowska-Kaczmar and Kubacki, 2005).”
• Applications of machine learning methods for engineering risk assessment – A review (2020, background): “The period between incident reporting, clearance time, and road clearance can be predicted by processing information rich textual data using machine learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), decision tree (DT), latent dirichlet allocation (LDA), radi”
• Review of Driving Performance Parameters Critical for Distracted Driving Research (2017, unknown): “Both fixations and smooth pursuit movements may reflect the how cognitive distraction interferes with how drivers acquire visual information (Liang et al., 2007).”
• Online Driver Distraction Detection Using Long Short-Term Memory (2011, methodology): “The predominant approach is to use static classifiers such as Support Vector Machines (SVM) [13], [18].”