# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Results
#### 4.3.1 Time Slice Classification  
In this Section we evaluate the classification performance of the three variants of the proposed approach (CNN, LSTM and GRU) at the time slice level and perform quantitative and qualitative comparisons with state-of-the-art. Figures 3-5 illustrate the training and validation loss of the proposed approach at the time slice level for CNN, LSTM, and GRU-based variants, respectively. The oscillations of the validation loss can be attributed to Adam optimization [31]. Besides this, there are no increasing parts in the validation loss, indicating that overfitting has been prevented. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Driver** & **Genre** & **Age Range** & **Model** & **Fuel** \\ \hline MTF1-D1 & Male & 30–40 & Toyota Yaris & Gasoline \\ MTF1-D2 & Male & 30–40 & Toyota Yaris & Gasoline \\ MTF2-D3 & Male & 30–40 & MERCEDES-41 hp & Diesel \\ MTF2-D4 & Male & 40–50 & IVECO-177 hp & Diesel \\ MTF2-D5 & Male & 40–50 & MERCEDES-41 hp & Diesel \\ MTF2-D6 & Male & 50–60 & IVECO-177 hp & Diesel \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Drivers and vehicles in the MOTIF-1 and MOTIF-2 datasets. Figure 3: Training/validation loss per epoch using the CNN-based variant for time slice classification on: (**a**) UAH, (**b**) MOTIF 1 and (**c**) MOTIF 2 datasets. Figure 4: Training/validation loss per epoch using the LSTM-based variant for time slice classification on: (**a**) UAH, (**b**) MOTIF 1 and (**c**) MOTIF 2 datasets. Table 4 presents the confusion matrices for the classification of time slices, as performed by the three variants of the proposed approach (CNN, LSTM and GRU) on each one of the three datasets described in Section 4.1 (UAH, MOTIF 1 and MOTIF 2). The overall classification results at the time slice level are summarized in Table 5. The most accurate classification is performed by GRU (accuracy 0.91), followed closely by LSTM (accuracy 0.89). Also, CNN tends to misclassify most aggressive time slices as normal. This latter behaviour also emerged in the preliminary work [17], and agrees with the intuition that both normal and aggressive drivers often seem to drive 'normally'. Still, the results of both LSTM-based and GRU-based components indicate that there are differences between  
Figure 5: Training/validation loss per epoch using the GRU-based variant for time slice classification on: (a) UAH, (b) MOTIF 1 and (c) MOTIF 2 datasets. normal and aggressive drivers in most time slices, which can be distinguished by certain NN-based encodings. It should be noted that classification at the time slice level cannot be regarded as a goal in itself in the context of this work. Rather than resulting in 'correctly' classified time slices, the intuition is to result in a time-series encoding which represents frequency of occurrence of driving patterns and is distinctive for each driving style. More so, taking into account that time slice labels are derived by the label of the containing route, essentially prohibiting a 'normal' time slice in an 'aggressive' route and vice-versa. In this sense, each NN-based component performing time slice classification is essentially evaluated by considering the labels at the route level. For example, when comparing two NN-based variants, variant A with lower classification accuracy at the time slice level, followed by higher classification accuracy at the route level, and variant B with higher classification accuracy at the time slice level, followed by lower classification accuracy at the route level, the encoding obtained by variant A generalizes better than the one obtained by variant B.