Discomfort detection during automated driving using temporal transformers - Assarzadeh, Maha; Hartwich, Franziska; Vitay, Julien; Bocklisch, Franziska; Hamker, Fred H. (Frontiers in Computer Science, 2025)
IntroductionWith the recent breakthroughs in driving automation and the development of smart vehicles, human-technology interaction issues, such as detecting comfort levels in automated driving, have been gaining increasing attention. Given the evidence of discomfort levels being an evolving psychological state in time, the tracking of discomfort levels for passengers of an automated vehicle can be considered a time-varying phenomenon.MethodsWe assessed a passenger's discomfort level in a smart, automated vehicle using physiological, environmental, and vehicle automation features from different sensors. Our approach is to dynamically predict discomfort levels using time-dependent models, particularly the Temporal Fusion Transformer (TFT), an advanced attention-based deep learning architecture providing an interpretable explanation of temporal dynamics as well as high-performance forecasting over multiple horizons. The models are trained and evaluated using a dataset of 100 participants of a simulated automated driving experiment, during which they signaled their level of discomfort using a manual device. Two TFT models, TFT-full and TFT-restricted, are investigated depending on which physiological, environmental, and vehicle automation signals are used as inputs. The results are compared with the auto-regressive model DeepAR. Different window sizes are used to analyze the impact of the window size on the model's performance.ResultsAmong the tested models, TFT-restricted with a window size of 300-time steps (about 5 s) demonstrates the best performance in predicting discomfort levels on our data, with a mean absolute error (MAE) of 0.037 and a root mean square error (RMSE) of 0.131.DiscussionIn our study, TFT-restricted outperformed TFT-full and the autoregressive model DeepAR in discomfort prediction, delivering superior results for all metrics. Finally, our study shows that the TFT can capture temporal dependencies in the data and help us interpret the model for detecting discomfort, which is essential for analyzing and improving people's acceptance of automated vehicles.