Abstract:

To overcome the limitation of lack of accuracy of the existing methods in assessing motion sickness caused by a single vehicle motion condition， this paper aims to construct a multidimensional motion sickness assessment model. Firstly， a real-vehicle experiment containing four typical working conditions is designed in this paper， such as acceleration and deceleration， and synchronously three-axis acceleration and angular velocity data of the vehicle as well as the passengers' postures is collected to construct a multi-source dataset. Secondly， the empirical mode decomposition （EMD） detrending method is used to deal with the subjective evaluation scale of motion sickness， so as to eliminate the cumulative trend of exposure time， and reveal the influence mechanism of motion sickness in a single motion condition. Finally， a BiLSTM-Transformer hybrid model is constructed to capture the long sequence time-series dynamic features using BiLSTM， extract the global dependencies using the Transformer self-attention mechanism， and perform the multi-dimensional dataset combination comparison and ablation experiments. The human-vehicle fusion parameters combined with the BiLSTM-Transformer model achieve 92.6% accuracy on the test set， with a test loss as low as 0.23. The results show that the model can effectively assess the occurrence of motion sickness， with an improvement in accuracy of 4.3% and 9.9%， respectively， compared to the single BiLSTM and Transformer models， demonstrating the advantages of the human-vehicle motion parameter fusion and BiLSTM-Transformer model in the assessment of motion sickness.

Key words: intelligent transportation, motion sickness assessment, deep learning, autonomous driving, comfort