# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 5 Conclusions  
This work introduces a hybrid representation approach for driving behaviour classification. The main idea is to combine data-driven classification methods with domain knowledge in the form of rules. The proposed approach combines NN-based encoding and rule-based event detection. Histograms derived from the output of these two components are concatenated and normalized to train a standard SVM, which is used to assess overall driving behavior. CNN, LSTM, and GRU architectures are employed in the context of different variants. The proposed approach is evaluated on the publicly available UAH dataset (2012), as well as on two datasets (MOTIF 1 and 2) created in the context of this work. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**State**} & \multirow{2}{*}{**Drier**} & \multirow{2}{*}{
\begin{tabular}{c} **Time** \\ **(min)** \\ \end{tabular} } & \multirow{2}{*}{**Km**} & **CNN** & **LSTM** & **GRU** & **Events** & **Hybrid** & **Hybrid** & **Hybrid** & **Romera** \\  & & & **(Only)** & **(Only)** & **(Only)** & **(Only)** & **(CNN)** & **(LSTM)** & **(GRU)** & **et al. (2012)** \\ \hline \multirow{8}{*}{Normal (Motorway)} & D1 & 14 & 25 & T & T & T & T & T & T & T \\  & D2 & 15 & 26 & F & T & T & T & T & T & T & T \\  & D3 & 15 & 26 & F & T & T & T & T & T & T & T \\  & D4 & 16 & 25 & T & T & T & T & T & T & T & T \\  & D5 & 15 & 25 & F & T & T & T & T & T & T & T \\  & D6 & 17 & 25 & T & T & T & T & T & T & T & T \\ \hline \multirow{8}{*}{Aggressive (Motorway)} & D1 & 12 & 24 & F & T & T & T & T & T & T & T \\  & D2 & 14 & 26 & T & F & F & T & F & T & T & F \\  & D3 & 13 & 26 & F & T & T & T & T & T & T & T \\  & D4 & 15 & 25 & T & F & F & T & T & T & T & F \\  & D5 & 13 & 25 & F & F & F & T & T & T & T & F \\  & D6 & 15 & 25 & F & T & T & T & T & T & T & T \\ \hline \multirow{8}{*}{Normal (Secondary)} & D1 & 10 & 16 & T & T & T & T & T & T & T & T \\  & D2 & 10 & 16 & T & F & T & T & T & F & T & T \\  & D3 & 11 & 16 & F & T & T & T & T & T & T & T \\  & D4 & 11 & 16 & T & F & F & T & T & T & T & T \\  & D5 & 11 & 16 & T & T & T & T & F & T & T & T \\  & D6 & 13 & 16 & T & T & T & T & T & T & T & T \\ \hline \multirow{8}{*}{Aggressive (Secondary)} & D1 & 8 & 16 & T & F & F & T & T & T & T & T \\  & D2 & 10 & 16 & T & T & T & T & T & T & T & T \\ \cline{1-1}  & D3 & 11 & 16 & F & F & F & F & F & F & F & T \\ \cline{1-1}  & D4 & 10 & 16 & F & T & T & F & F & T & T & T \\ \cline{1-1}  & D5 & 7 & 12 & T & T & T & T & F & T & T & T \\ \hline \multicolumn{1}{c}{Accuracy at Normal} & \multicolumn{1}{c}{8/12} & 10/12 & 11/12 & 12/12 & 11/12 & 11/12 & 12/12 & 12/12 \\ \hline \multicolumn{1}{c}{Accuracy at Aggressive} & \multicolumn{1}{c}{5/11} & 6/11 & 6/11 & 9/11 & 6/11 & 10/11 & 10/11 & 8/11 \\ \hline \multicolumn{1}{c}{Overall Accuracy} & \multicolumn{1}{c}{13/23} & 16/23 & 17/23 & 21/23 & 17/23 & 21/23 & 22/23 & 20/23 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Route level classification on UAH dataset. Such a hybrid approach has not appeared in the literature of driving behaviour analysis computational tools. Other novel elements of the proposed approach include the use of image-like representations of sensor measurements in the case of the CNN-based variant, as well as the first application of GRUs in driving behaviour analysis. The main conclusions derived from our experiments can be summarized as follows:  
1. Both NN-guided time-series encoding and rule-guided event detection contribute to the accuracy obtained by the proposed hybrid classification method. 2. The RNN-based variants (LSTM and GRU) obtain higher classification performance than the CNN-based variants, more so in the UAH and MOTIF 2 datasets. 3. Between LSTM and GRU, the latter achieves slightly more accurate classification. 4. The GRU-based variant obtains time slice classification, which exceeds 90% accuracy, without using data derived from camera, as is the case with other state-of-the-art approaches [11; 18]. 5.