## 4 Experiments
### Learning Capability  
As mentioned earlier, in this study, the data collected by the first participant on general roads were divided into 73 clusters, the second participant's data into 62 clusters, and the third participant's data into 67 clusters. For highway driving, the first participant's data were divided into 58 clusters and the second into 56. This experiment hopes to understand whether the ANM system has considerable learning capabilities for each data cluster. In other words, can we use the ANM system for judgment learning for each type of driving behavior? That is to say, we can use the driving behavior of the first 25 s to judge the reaction behavior of the next 25 s. If the preliminary experimental results prove that the above inference is feasible, we can gradually increase the driver's behavior. On the other hand, we can gradually increase the system's complexity according to the needs of the problem domain (for example, different weather, traffic flow, and road conditions). In this experiment, we randomly selected 20 clusters from the 73 clusters of the first participant in a general road environment. Additionally, nine clusters were selected from the other two participants in a general road environment. For the highway data, nine clusters were selected from the two participants in a heavy and light traffic environment. Each cluster also randomly selected a driving clip to test whether the relationship between the first 25 s and the last 25 s of driving behavior can be established. The results showed that the degree of learning improvement was relatively rapid in the early stages of learning but slowed down in the later stages. The degree of improvement became smaller and smaller in the last stages. Most importantly, however, the system showed continued improvement, even in the later stages of learning. We allowed the ANM system's learning to terminate when the learning improvement was relatively slow. The results show (Tables 1-7) that the learning results in each cluster are above 75%. We add here that if the system can continue learning, there is still room for  
Figure 14: Input/Output interface of the ANM system. continuous improvement (i.e., higher accuracy). However, the experimental results in this part of the article were suspended at appropriate times to explore the different information processing of ANM systems more broadly. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cluster** & **Run** & \begin{tabular}{c} **Loss at** \\ **Cycle 1** \\ \end{tabular} & \begin{tabular}{c} **Loss at** \\ **Termination** \\ \end{tabular} & \begin{tabular}{c} **Improvement** \\ **Rate** \\ \end{tabular} & **Run** & \
\begin{tabular}{c} **Loss at** \\ **Cycle 1** \\ \end{tabular} &
\begin{tabular}{c} **Loss at** \\ **Termination** \\ \end{tabular} & **Improvement Rate** \\ \hline
1 & 3 & 3933.0 & 948.0 & 75.9\% & 33 & 3161.0 & 559.0 & 82.3\% \\ \hline
2 & 6 & 4082.4 & 863.3 & 78.9\% & 36 & 3410.0 & 744.0 & 78.2\% \\ \hline
3 & 9 & 3289.0 & 756.0 & 77.0\% & 39 & 2937.0 & 739.0 & 75.0\% \\ \hline
4 & 12 & 4139.1 & 848.4 & 79.5\% & 42 & 2933.0 & 975.0 & 66.8\% \\ \hline
5 & 15 & 3979.0 & 956.0 & 76.0\% & 45 & 3104.0 & 730.0 & 76.5\% \\ \hline
6 & 18 & 4676.1 & 864.5 & 81.5\% & 48 & 3057.6 & 537.9 & 82.4\% \\ \hline
7 & 21 & 4316.0 & 661.0 & 84.7\% & 51 & 3039.0 & 290.0 & 90.5\% \\ \hline
8 & 24 & 4759.3 & 772.0 & 83.8\% & 54 & 2866.2 & 460.3 & 83.9\% \\ \hline
9 & 27 & 5792.0 & 879.0 & 84.8\% & 57 & 3425.0 & 1068.0 & 68.8\% \\ \hline
10 & 30 & 4230.6 & 537.4 & 87.3\% & 60 & 4132.0 & 957.0 & 76.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Participant A’s performance during the first cycle and at termination while driving in a general road environment.