1 citations · 2 across the 2 of their papers we have counts for
4 papers
Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model
Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani +3
One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving. Although physiological signals have b…
Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning
Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani +2
Motion simulators allow researchers to safely investigate the interaction of drivers with a vehicle. However, many studies that use driving simulator data to predict cognitive load…
Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing
Camilo Gonzalez Arango, Houshyar Asadi, Mohammad Reza Chalak Qazani +1
Motion Cueing Algorithms (MCAs) encode the movement of simulated vehicles into movement that can be reproduced with a motion simulator to provide a realistic driving experience wit…
Neural Networks for Fast Optimisation in Model Predictive Control: A Review
Camilo Gonzalez, Houshyar Asadi, Lars Kooijman +1
Model Predictive Control (MPC) is an optimal control algorithm with strong stability and robustness guarantees. Despite its popularity in robotics and industrial applications, the…