# Towards Explainable Pedestrian Behavior Prediction:
## 5 Experimental Results and Discussion
### Analysis of the Explainability of the Experimental Results
Regarding explainability, we performed an analysis using the best performing configuration, **J10K-P10K** composed by 64 rules to examine the fuzzy rules activated by the pedestrian behavior predictor when assessing pedestrian crossing behavior in the \(JAAD_{beh}\) and \(PIE_{beh}\) test sets. As is shown in Figure 10, over the \(JAAD_{beh}\) it is evident that three primary rules (Rule 58, Rule 46, and Rule 61) are activated most frequently, while the remaining rules display a relatively similar activation percentage. In contrast, within the \(PIE_{all}\) dataset, there is a significant disparity between the most frequently activated rule and the other fourteen. Rule 1 was the most frequently activated, occurring 7.34% of the time. \begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Configuration/Model**} & \multicolumn{2}{c}{\(JAAD_{all}\)} & \multicolumn{2}{c}{\(PIE_{all}\)} \\ \cline{2-5}  & **A** & **F** & **A** & **F** \\ \hline J10K-P10K & 0.69 & 0.71 & 0.80 & 0.56 \\ J8 KR3 & 0.75 & **0.79** & 0.73 & 0.51 \\ ConvLSTM [8] & 0.63 & 0.32 & 0.58 & 0.39 \\ SingleRNN [3] & 0.65 & 0.34 & **0.83** & **0.67** \\ C3D [6] & **0.84** & 0.65 & 0.77 & 0.52 \\ \hline \end{tabular}
\end{table} 
Table 9: Comparative results with state-of-the-art models. Accuracy (A) and F1 score. Bold values indicate the best performance for each evaluation metric across the different configurations. Figure 9: Confusion matrix for J10K-P10K configuration. Figure 11 presents the distribution and impact of pedestrian features on the most activated fuzzy rules. From an explainability perspective, the most frequently activated rules offer preliminary insights into why a pedestrian chooses to cross and how explainable features support decision-making transparency. **Proximity** and **Distance** emerge as the most influential factors as evidenced by the high weights in rules where these features are present. **Orientation** plays a significant role in decision-making, but contradictory rules indicate that it must be analyzed in combination with other features. While orientation provides directional context, its influence varies depending on additional conditions. Pedestrians facing the road are more likely to cross, whereas those walking forward or away tend to refrain from crossing. Pedestrian **Action** serves as a strong supporting feature but is not a standalone predictor. Some pedestrians may walk near the road without actually crossing, highlighting the need for additional contextual cues. Finally, **Attention** and **Zebra Crossing** Presence (CrossLevel) contribute to the prediction but act as secondary factors. Rather than determining pedestrian behavior outright, they refine and enhance the accuracy of predictions, complementing the primary features. Figure 11: Feature activation heatmap over the most activated fuzzy rules. Figure 10: Top 15 fuzzy rules activation. To visually illustrate the model's output, Figure 12 presents an example prediction from the JAAD test set. In this example, the pedestrian is about to cross the road, and the figure showcases how the neuro-symbolic approach predicts the probability of crossing across different frames. Additionally, it highlights the most activated rules and the key features that influenced the prediction, providing valuable insight into the model's decision-making process. For each frame, the linguistic values of each explainable feature are displayed, along with a bar indicating the feature's relevance based on the activated rules. The impact of proximity, distance, and orientation is evident throughout the example. Notably, proximity becomes particularly significant when the probability of crossing is high, reinforcing its crucial role in pedestrian behavior prediction.