# Towards Explainable Pedestrian Behavior Prediction:
## 5 Experimental Results and Discussion
### Analysis of the Explainability of the Experimental Results  
Based on the results obtained from the five experiments mentioned, we have summarized the best performing configurations of the pedestrian behavior predictor. These results are presented in Table 8 and include fuzzy rules trained with the following datasets: (1) 14,000 records from ordered JAAD videos (**J14K**), (2) 14,000 records from randomly selected JAAD videos (**J14 KR3**), and (3) a combined dataset of 10,000 records from PIE and 10,000 records from JAAD (**J10K-P10K**). It is important to note that the results of these configurations are associated with balanced mispredictions between the two pedestrian behaviors (crossing or not crossing) as can be observed in the confusion matrix shown in Figure 9. This confusion matrix was elaborated from the results provided using the J10K-P10K configuration. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Rules**} & \multicolumn{2}{c}{\(\boldsymbol{JAAD_{all}}\)} & \(\boldsymbol{JAAD_{beh}}\) & \multicolumn{2}{c}{\(\boldsymbol{PIE_{all}}\)} & \multicolumn{2}{c}{\(\boldsymbol{PIE_{beh}}\)} \\ \cline{3-10}  & & **A** & **F** & **A** & **F** & **A** & **F** & **A** & **F** \\ \hline J14K & **58** & **0.72** & **0.75** & **0.72** & **0.76** & 0.73 & 0.50 & 0.73 & 0.52 \\ P80K & 36 & 0.58 & 0.54 & 0.56 & 0.54 & 0.81 & 0.54 & 0.81 & 0.57 \\ J8K-P8K & 61 & 0.72 & 0.74 & 0.71 & 0.75 & 0.79 & 0.54 & 0.79 & 0.57 \\ J10K-P10K & **64** & 0.69 & 0.71 & 0.68 & 0.71 & 0.80 & **0.56** & 0.81 & **0.60** \\ J14K-P80K & 62 & 0.56 & 0.45 & 0.54 & 0.45 & **0.84** & 0.51 & **0.82** & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experiment 5— diverse datasets results. Accuracy (A) and F1 score. Bold values indicate the best performance for each evaluation metric across the different configurations. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Rules**} & \multicolumn{2}{c}{\(\boldsymbol{JAAD_{all}}\)} & \(\boldsymbol{JAAD_{beh}}\) & \multicolumn{2}{c}{\(\boldsymbol{PIE_{all}}\)} & \multicolumn{2}{c}{\(\boldsymbol{PIE_{beh}}\)} \\ \cline{3-10}  & & **A** & **F** & **A** & **F** & **A** & **F** & **A** & **F** \\ \hline J10K-P10K & **64** & 0.69 & 0.71 & 0.68 & 0.71 & **0.80** & **0.56** & **0.81** & **0.60** \\
**J8 KR3** & **70** & **0.75** & **0.79** & **0.74** & **0.77** & 0.73 & 0.51 & 0.73 & 0.53 \\ J14K & 58 & 0.72 & 0.75 & 0.72 & 0.76 & 0.73 & 0.50 & 0.73 & 0.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best results over all experiments. Accuracy (A) and F1 score. Bold values indicate the best performance for each evaluation metric across the different configurations. In the JAAD dataset, mispredictions occur in 26% of cases where the model predicts a pedestrian will cross when they actually will not, and in 29% of cases where the model predicts a pedestrian will not cross when they actually will. In contrast, for the PIE dataset, 14% of predictions incorrectly classify a pedestrian as crossing when they are not, while 35% of predictions fail to detect actual crossing behavior. This analysis suggests that the F1 score remains unaffected because the predictor achieves high accuracy in at least one of the classification categories, balancing the overall performance despite specific misclassifications trends. Table 9 presents a comparison between the best results we obtained and some state-of-the-art approaches based on the benchmark [9]. According to the table, although the neuro-symbolic approach does not show a significant improvement in terms of accuracy and F1 score, the key advantage of our method lies in producing explainable predictions, from the input to the final result. Nevertheless, our model J8 KR3 provides more consistent results on the JAAD dataset.