Fig. 1

Overview of the proposed neural network model for evaluating driving aptitude. The proposed network is composed of two parts: a dimensionality reduction layer with \(L_1\) regularized weights and a log-linearized Gaussian mixture network (LLGMN) [28]. This network calculates the posterior probability \(p(c|\textbf{x})\) of the presence or absence of driving aptitude (i.e., drivable or undrivable) using the indices \(\textbf{x}\) obtained from the physical and cognitive function tests as input. The weight parameters of the dimensionality reduction layer are denoted by \(\textbf{w} = \{w_i\}_{i=1}^P\)