Detecting drivers’ cognitive load from saccadic intrusion

Author:

Biswas Pradipta,Prabhakar Gowdham

Publisher

Elsevier BV

Subject

Applied Psychology,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference26 articles.

1. Characteristics of saccadic intrusions;Abadia;Vision Research,2004

2. Afzal, S., & Robinson, P. (2009). Natural affect data – collection & annotation in a learning context. In Proceedings of Affective Computing & Intelligent Interaction (ACII), Amsterdam.

3. Basir, O., Bhavnani, J. P., Karray, F., & Desrochers, K. (2004). Drowsiness detection system, US 6822573 B2.

4. Boril, H., Sadjadi, S. O., & Hansen, J. H. L. (2011). UTDrive: Emotion and cognitive load classification in-vehicle scenarios. In Proceeding of the 5th biennial workshop on DSP for in-vehicle systems.

5. Some hazards are more attractive than others: Drivers of varying experience respond differently to different types of hazard;Crundall;Accident Analysis and Prevention,2012

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