# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Results
#### 4.3.1 Time Slice Classification
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & UAH & \\ \hline \multirow{3}{*}{CNN} & \multirow{3}{*}{Normal Aggressive} & Normal & Aggressive \\  & Normal & 0.85 & 0.15 \\  & Aggressive & 0.70 & 0.30 \\ \hline \multirow{3}{*}{LSTM} & \multirow{3}{*}{Normal Aggressive} & Normal & Aggressive \\  & Normal & 0.97 & 0.03 \\  & Aggressive & 0.25 & 0.75 \\ \hline \multirow{3}{*}{GRU} & \multirow{3}{*}{Normal Aggressive} & Normal & Aggressive \\  & Normal & 0.98 & 0.02 \\  & Aggressive & 0.22 & 0.78 \\ \hline \multirow{3}{*}{CNN} & \multirow{3}{*}{Normal Aggressive} & Normal & Semi-aggressive \\  & Normal & 0.92 & 0.05 & 0.03 \\  & Semi-aggressive & 0.14 & 0.83 & 0.03 \\  & Aggressive & 0.03 & 0.04 & 0.94 \\ \hline \multirow{3}{*}{LSTM} & \multirow{3}{*}{Normal Aggressive} & Normal & Semi-aggressive & Aggressive \\  & Normal & 1.00 & 0 & 0 \\  & Semi-aggressive & 0.005 & 0.99 & 0.005 \\  & Aggressive & 0 & 0.02 & 0.98 \\ \hline \multirow{3}{*}{GRU} & \multirow{3}{*}{Normal Aggressive} & Normal & Semi-aggressive & Aggressive \\  & Normal & 1.00 & 0 & 0 \\  & Aggressive & 0 & 0.01 & 0.99 \\ \hline \multirow{3}{*}{CNN} & \multirow{3}{*}{Normal Aggressive} & Normal & Semi-aggressive & Aggressive \\  & Normal & 0.86 & 0.07 & 0.07 \\  & Semi-aggressive & 0.15 & 0.81 & 0.04 \\  & Aggressive & 0.08 & 0.16 & 0.76 \\ \hline \multirow{3}{*}{LSTM} & \multirow{3}{*}{Normal Aggressive} & Normal & Semi-aggressive & Aggressive \\  & Normal & 0.95 & 0.03 & 0.02 \\  & Semi-aggressive & 0.01 & 0.98 & 0.01 \\  & Aggressive & 0 & 0.01 & 0.99 \\ \hline \multirow{3}{*}{GRU} & \multirow{3}{*}{Normal Aggressive} & Normal & Semi-aggressive & Aggressive \\  & Normal & 0.98 & 0.01 & 0.01 \\  & Semi-aggressive & 0.03 & 0.96 & 0.01 \\  & Aggressive & 0.03 & 0.01 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrices for time slice classification. Other works, sharing similar elements with the proposed approach, have been applied on the UAH dataset focusing on time slice classification. [PERSON] et al. [11] report an F1 measure of 91%, which is equal to the one reported here for the GRU-based variant (Table 5). [PERSON] et al. additionally identify drowsy driving behaviour, however this is achieved by employing mobile phone camera in order to identify lane drifting, which has been acknowledged [12] as a strong indicator for this type of driving behaviour. On a similar setting, [PERSON] and [PERSON] [18] report an F1 measure exceeding 99%. This is the highest score reported in the literature for time slice classification, however it is also obtained by using mobile phone camera. The CNN-based approach of [PERSON] et al. [7] is also evaluated on the UAH dataset, however it is not quantitatively comparable since it addresses a different problem: maneuver classification. Overall, the proposed hybrid approach can be viewed as a framework to combine data-driven classification with domain knowledge. As such, it could potentially encompass other classification approaches introduced in the literature, as is the approach of [PERSON] and [PERSON], in order to address time slice classification, as well as overall route classification (see Section 4.3.2).