# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 5 Conclusions  
We propose a system that broadcasts the predicted behavior through V2X with a 4-layer CNN-2 stacked LSTM for driving behavior classification using only 3-axis acceleration. For 4-layer CNN-2 stacked LSTM training, 15% of real world DGIST campus driving data and 85% of virtual world driving data were used. The virtual world is similar to the real world, including about 240 traffic vehicles with randomly assigned driving motions, such as speeding and lane changes, as well as uphill, downhill and speed limit road environments. The virtual world driving data was collected by building DGIST campus, urban road and highway based on the connection between IPG CarMaker and a driving simulator that mimics real vehicles. The performance of the proposed system showed  
Figure 17: V2X communication delay distribution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test: Real World DGIST Campus** & \multicolumn{4}{c}{**Output Label**} \\ \hline
**Input 5 s** & **Networks** & **Defensive** & **Normal** & **Aggressive** \\ \hline \multirow{6}{*}{Defensive} & 1D-CNN & 97.51\% & 2.13\% & 0.36\% \\  & 2D-CNN & 99.15\% & 0.64\% & 0.21\% \\  & LSTM & 90.58\% & 6.51\% & 2.92\% \\  & CNN-LSTM & 95.8\% & 4.2\% & 0.0\% \\  & 4 CNN-LSTM & 98.08\% & 1.53\% & 0.39\% \\  & 4 CNN-2 LSTM & 98.65\% & 0.428\% & 0.002\% \\ \hline \multirow{6}{*}{Normal} & 1D-CNN & 2.49\% & 96.93\% & 0.58\% \\  & 2D-CNN & 2.7\% & 96.41\% & 0.89\% \\  & LSTM & 54.67\% & 21.44\% & 23.89\% \\  & CNN-LSTM & 2.31\% & 97.24\% & 0.44\% \\  & 4 CNN-LSTM & 0.62\% & 98.49\% & 0.89\% \\  & 4 CNN-2 LSTM & 0.013\% & 99.82\% & 0.04\% \\ \hline \multirow{6}{*}{Aggressive} & 1D-CNN & 0.64\% & 1.86\% & 97.51\% \\  & 2D-CNN & 1.37\% & 2.37\% & 96.26\% \\ \cline{1-1}  & LSTM & 13.47\% & 14.85\% & 71.69\% \\ \cline{1-1}  & CNN-LSTM & 0.37\% & 8.54\% & 91.09\% \\ \cline{1-1}  & 4 CNN-LSTM & 0.21\% & 1.11\% & 98.67\% \\ \cline{1-1}  & 4 CNN-2 LSTM & 0.0\% & 0.16\% & 99.84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion matrix of driving behavior classification results in real world DGIST campus. the best performance compared to 1D-CNN, 2D-CNN, LSTM, CNN-LSTM, and 4-layer CNN-LSTM, which are state-of-the-art techniques. The effectiveness of sharing driving behavior was evaluated through ACC scenarios constructed in simulations. The desired distance of the ego vehicle is changed through the assigned safety factor, according to the driving behavior of the target vehicle. If the target vehicle has aggressive driving behavior, the ego vehicle increases the desired distance. Compared to the case where the proposed system was not applied, the TTC increased by about 3 s or more. To evaluate the proposed system in the real world, we built a prototype, including IMU and Jetson TX2 board. As a result of driving tests on DGIST campus, it had a driving behavior classification performance of about 97% or more. The classified driving behavior is mounted on the vehicle BSM conforming to the J2735 standard via V2X communication and transmitted to nearby vehicles. As a result of the transmission and reception of the BSM, the expected driving behavior was shared with surrounding vehicles with a small delay of 4.8 ms on average. Therefore, systems and prototypes that classify driving behavior with only 3-axis accelerations and share it with the traffic system have been validated. Simulation tests and real world tests confirmed that the proposed system can help traffic safety. Through this, the development of various driver support systems, such as a system that warns the driver when the driver inputs an aggressive driving command, and more intelligent ACC systems, can be expected. Although a comprehensive classification of driving aggression has been performed, a more detailed classification may be necessary for driving safety. For example, there may be a scenario of changing lanes without a signal or changing several lanes at once.