# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 4 Performance Evaluation
### Simulation Result of Driving Behavior Prediction  
The performance of the trained networks is evaluated through the validation data and the simulation test data. We present the validation results at the input of 5 s, because the case of 1 to 4 s of input is significantly lower accuracy than the case of 5 s of input. Table 5 is the network performance indicator when the input timeseries is 5 s. The classification accuracy for the defensive behavior is about 92% or more in all networks, especially the CNN-LSTM network, the accuracy is improved by about 11% compared to when the input timeseries is 4 s. For normal behavior, the CNN network has a classification error of about 29% as defensive behavior. As a result of the aggressive behavior, 2D-CNN had a classification accuracy of about 78% when the input time series was 4 s, but showed a steep performance improvement to about 99% when the input timeseries was 5 s. The 4-layer CNN-2 stacked LSTM has an average accuracy of 98.52%, and the performance improvement is about 0.39% compared to the 98.13% of the 4-layer CNN-LSTM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Networks** & 1D-CNN & 2D-CNN & LSTM \\
**Training Time (s)** & 17.82742023 & 476.4470894 & 163.4962823 \\ \hline
**Networks** & CNN-LSTM & 4 CNN-LSTM & 4 CNN-2 LSTM \\
**Training Time (s)** & 37.72614694 & 92.75716472 & 75.77209926 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The networks training time at 50 epochs. Figure 13: Training accuracy input length: (**a**) Timeseries 1 s. (**b**) Timeseries 2 s. (**c**) Timeseries 3 s. (**d**) Timeseries 4 s. (**e**) Timeseries 5 s. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Validation**} & \multicolumn{3}{c}{**Output Label**} \\ \hline
**Input: 5 s** & **Networks** & **Defensive** & **Normal** & **Aggressive** \\ \hline \multirow{6}{*}{Defensive} & 1D-CNN & 92.87\% & 6.49\% & 0.63\% \\  & 2D-CNN & 95.04\% & 0.29\% & 4.66\% \\  & LSTM & 93.02\% & 4.13\% & 2.86\% \\  & CNN-LSTM & 94.24\% & 5.76\% & 0.00\% \\  & 4 CNN-LSTM & 97.02\% & 2.91\% & 0.07\% \\  & 4 CNN-2 LSTM & 99.52\% & 0.41\% & 0.07\% \\ \hline \multirow{6}{*}{Normal} & 1D-CNN & 29.4\% & 67.23\% & 3.37\% \\  & 2D-CNN & 0.32\% & 91.56\% & 8.11\% \\ \cline{1-1}  & LSTM & 62.23\% & 18.53\% & 19.24\% \\ \cline{1-1}  & CNN-LSTM & 19.92\% & 79.92\% & 0.16\% \\ \cline{1-1}  & 4 CNN-LSTM & 1.78\% & 97.92\% & 0.29\% \\ \cline{1-1}  & 4 CNN-2 LSTM & 3.29\% & 96.39\% & 0.32\% \\ \hline \multirow{6}{*}{Aggressive} & 1D-CNN & 3.39\% & 6.89\% & 89.72\% \\  & 2D-CNN & 0.21\% & 0.07\% & 99.73\% \\ \cline{1-1}  & LSTM & 15.83\% & 9.22\% & 74.95\% \\ \cline{1-1}  & CNN-LSTM & 0.65\% & 4.08\% & 95.27\% \\ \cline{1-1}  & 4 CNN-LSTM & 0.14\% & 0.41\% & 99.45\% \\ \cline{1-1}  & 4 CNN-2 LSTM & 0.16\% & 0.19\% & 99.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation confusion matrix at 5 s timeseries input. From the validation process, we select the input of timeseries 5 s that gives the best performance for classifying driving behavior. The trained networks are evaluated for performance in each environment through test data collected from DGIST campus, urban roads and highways in the simulation. Tables 6-8 are the results of network evaluation based on the test data generated in each simulation environment. The classification accuracy of proposed 4-layer CNN-2 stacked LSTM is more about 99% on highways that are simply composed of straight roads. In addition, on DGIST campuses and urban roads with various road types, the classification accuracy of the proposed network is relatively reduced compared to highways, but the classification accuracy is \(\sim\)97% or higher on average. The network with the second highest classification performance is a 4-layer CNN-LSTM, which has a reduced performance of 1.3% in the DGIST campus scenario, 0.31% in the urban scenario, and 3.08% in the highway compared to our proposed network.