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ObjectiveOur objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. BackgroundControl transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. MethodWe used data from a driving simulation to develop an active inference model of takeover performance. After validating the model’s predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. ResultsThe model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. ConclusionThe results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. ApplicationThe active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety. 

Optical analog computation is garnering increasing attention due to its innate parallel processing capabilities, swift computational speeds, and minimal energy requirements. However, traditional optical components employed for such computations are usually bulky. Recently, there has been a substantial shift toward utilizing nanophotonic structures to downsize these bulky optical elements. Nevertheless, these nanophotonic structures are typically realized in planar subwavelength nanostructures, demanding intricate fabrication processes and presenting limitations in their numerical apertures. In this study, we present a three-layer thin-film optical coating different from the conventional Fabry–Pérot nanocavity. Our design functions as a real-time Laplacian operator for spatial differentiation, and it remarkably boasts an ultrahigh numerical aperture of up to 0.7, enabling the detected edges to be sharper and have closely matched intensities. We also experimentally demonstrate its capacity for effective edge detection. This ultracompact and facile-to-fabricate thin-film spatial differentiator holds promising prospects for applications in ultrafast optical processing and biomedical imaging.