Recognition of driver's mental workload based on physiological signals, a comparative study. (January 2022) - Huang, Jing; Liu, Yu; Peng, Xiaoyan
Abstract: It tends to invite road accidents for automotive drivers when they drive at a too high or too low level of mental workload. So it's rewarding to recognize driver's mental workload so that providing decision basis to driving assistance system of vehicles to warn drivers or even, take over driving. In this study, we conducted simulated driving experiment and collected driver's various physiological signals under different driving conditions. A comparison was made between machine learning and deep learning methods of the recognizing task. Driver's physiological signal samples of different lengths were tested and the accuracy of which were compared. The results indicate that, the deep learning model based on a combination of CNN and LSTM gets a higher accuracy rate than the others, and methods based on deep learning have a better performance than that based on manual feature extraction and traditional classifier.