## 4 Experiments
### Fatigued Driving  
This study sorted out the possible symptoms of driver fatigue from relevant literature, including blurred vision, involuntary nodding, increasing eye closing frequency, longer eye closing time, continuous yawning, facial numbness, slow eye reaction, stiff movements, changing lanes at will, etc. Based on the current settings of this study, this experiment assumes that drivers are prone to three possible fatigue driving conditions: changing lanes at will, eyes wandering and closing, and slow reaction speed. The first case (eyes wandering and closing) tests eye movement information, while the last two instances (switching lanes at will and slow reaction speed) exist mainly to explore the effects caused by abnormal pressure on the hands and feet. The first case is to ask subjects to move and close their eyes while driving deliberately. The primary purpose of this experiment is to observe the impact of changes in eye movement. The second case (switching lanes at will) is simulated by asking the subjects to switch lanes at will many times while driving, while the third case (slow reaction speed) is simulated by asking the subjects to react with slow hand and foot reactions. As before, the settings for all driving environments are the same. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**Run**} & **Loss at Cycle 1** & **Loss at Termination** & **Improvement Rate** \\ \hline
1 & 6 & 1214.6 & 877.5 & 27.8\% \\ \hline
2 & 12 & 1395.9 & 880.8 & 36.9\% \\ \hline
3 & 18 & 674.3 & 455.2 & 32.5\% \\ \hline
4 & 24 & 1461.4 & 839.0 & 42.6\% \\ \hline
5 & 30 & 1541.7 & 531.2 & 65.5\% \\ \hline
6 & 36 & 766.1 & 550.6 & 28.1\% \\ \hline
7 & 42 & 718.9 & 512.8 & 28.7\% \\ \hline
8 & 48 & 1351.0 & 906.1 & 32.9\% \\ \hline
9 & 54 & 1497.5 & 875.8 & 41.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 18: Participant B’s data in a heavy traffic environment were tested with Participant A’s learned system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & \multirow{2}{*}{**Run**} & **Loss at Cycle 1** & **Loss at Termination** & **Improvement Rate** \\ \hline
5 & 30 & 1081.9 & 584.3 & 46.0\% \\ \hline
6 & 36 & 951.2 & 567.6 & 40.3\% \\ \hline
7 & 42 & 1156.4 & 489.2 & 57.7\% \\ \hline
8 & 48 & 1257.6 & 792.2 & 37.0\% \\ \hline
9 & 54 & 1697.7 & 750.6 & 55.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 17: _Cont._driving clips collected in this experiment's first, second, and third cases are 40, 43, and 63, respectively. Each case is tested separately in this experiment, and the average value is taken. The average loss obtained in each case is relatively high (please refer to the loss value in Table 19).  
In the following, we further explore whether these different distraction phenomena can be further trained through the ANM system. Similar to the second experiment, we took 6 of the 20 learned systems to conduct the above research. For each distraction case, five tests were performed. For each test, we first observed the loss values when the system faced driver distraction and then allowed the system to run for 500 generations to observe the final results. The results are shown in Table 20. After 500 generations of learning, some results can be good, while others still have considerable room for effort. However, looking at these data, there is still room for learning. However, the distraction test is a semi-random variation. There is still considerable room for discussion, or a certain degree of controversy, in trying to draw concrete conclusions from this approach.  
Figure 17: Loss values at different noise levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distraction Case** & **No. of Clips** & **Average Loss** \\ \hline Eyes wandering and closing & 40 & 3293.1 \\ \hline Switching lanes at will & 43 & 3452.3 \\ \hline Slow reaction speed & 63 & 3457.7 \\ \hline \hline \end{tabular}
\end{table}
Table 19: The average loss of each distraction case.