# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Results
#### 4.3.2 Route Level Classification
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & UAH & \\ \hline \multirow{3}{*}{CNN} & & Normal & Aggressive \\  & Normal & 11/12 (8/12) & 1/12 (4/12) \\  & Aggressive & 4/11 (6/11) & 7/11 (5/11) \\ \hline \multirow{3}{*}{LSTM} & & Normal & Aggressive \\  & Normal & 11/12 (10/12) & 1/12 (2/12) \\  & Aggressive & 1/11 (5/11) & 10/11 (6/11) \\ \hline \multirow{3}{*}{GRU} & & Normal & Aggressive \\  & Normal & 12/12 (11/12) & 0/12 (1/12) \\  & Aggressive & 1/11 (5/11) & 10/11 (6/11) \\ \hline \multicolumn{3}{c}{MOTIF 1} \\ \hline \multirow{3}{*}{CNN} & & Normal & Semi-aggressive & Aggressive \\  & Normal & 10/11 (11/11) & 1/11(0/11) & 0/11 (0/11) \\  & Semi-aggressive & 0/11 (0/11) & 11/11 (9/11) & 0/11 (2/11) \\  & Aggressive & 0/11 (0/11) & 0/11 (2/11) & 11/11 (9/11) \\ \hline \multirow{3}{*}{LSTM} & & Normal & Semi-aggressive & Aggressive \\  & Normal & 11/11 (5/11) & 0/11 (6/11) & 0/11 (0/11) \\  & Semi-aggressive & 0/11 (2/11) & 11/11 (7/11) & 0/11 (2/11) \\  & Aggressive & 0/11 (0/11) & 1/11 (1/11) & 10/11 (10/11) \\ \hline \multirow{3}{*}{GRU} & & Normal & Semi-aggressive & Aggressive \\  & Normal & 11/11 (5/11) & 0/11 (6/11) & 0/11 (0/11) \\  & Semi-aggressive & 0/11 (2/11) & 11/11 (7/11) & 0/11 (2/11) \\  & Aggressive & 0/11 (0/11) & 0/11 (0/11) & 11/11 (11/11) \\ \hline \multicolumn{3}{c}{MOTIF 2} \\ \hline \multirow{3}{*}{CNN} & & Normal & Semi-aggressive & Aggressive \\  & Normal & 7/12 (4/12) & 1/12 (8/12) & 4/12 (0/12) \\  & Semi-aggressive & 1/12 (0/12) & 11/12 (12/12) & 0/12 (0/12) \\  & Aggressive & 0/12 (0/12) & 0/12 (2/12) & 12/12 (10/12) \\ \hline \multirow{3}{*}{LSTM} & & Normal & Semi-aggressive & Aggressive \\  & Normal & 10/12 (9/12) & 2/12 (3/12) & 0/12 (0/12) \\  & Semi-aggressive & 1/12 (1/12) & 11/12 (11/12) & 0/12 (0/12) \\  & Aggressive & 0/12 (0/12) & 0/12 (0/12) & 12/12 (12/12) \\ \hline \multirow{3}{*}{GRU} & & Normal & Semi-aggressive & Aggressive \\  & Normal & 11/12 (10/12) & 1/12 (2/12) & 0/12 (0/12) \\  & Semi-aggressive & 0/12 (0/12) & 12/12 (12/12) & 0/12 (0/12) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrices for route level classification (NN-only result in parentheses). is increased, as evident in the results obtained by the 'Hybrid' counterparts. Also, there is a case in which 'NN-only' variants 'correct' 'Events-only' ('D4-Aggressive-Secondary'). These observations demonstrate that each component contributes complementary information, increasing overall classification accuracy. It should be noted that the results of the hybrid classification variants, as well as of the 'NN-only' and 'Events-only' variants, which have been introduced in the context of this work, were obtained using acceleration measures as input. On the other hand, the method of [PERSON] et al. (2012) uses all smartphone sensors (inertial sensors, camera, GPS, and internet access), in order to log and recognize driving maneuvers and infer behaviour. In addition, [PERSON] et al. identify drowsy driving behaviour, but as is the case with [PERSON] et al. (2011), this is achieved by using mobile phone camera in order to identify lane drifting, which has been acknowledged by the authors as a strong indicator for this type of driving behaviour. In the work of [PERSON] et al., there are no misclassifications of either normal or aggressive routes as drowsy, in order to affect the results presented on Table 7.