## 4 Experiments
### Pedestrian Hazardous Crossing Intention Recognition and Prediction Experiment
As shown in Table 6, our constructed IPVO-LSTM algorithm outperformed the other two algorithms overall in all evaluation metrics for pedestrian hazardous crossing intention recognition in view-obscured areas (in our study, the parameters of the RF were chosen as follows: the number of classifiers was 10, and the maximum depth of the decision tree was 15; the parameters of the LSTM and Bi-LSTM were selected as follows: the number of hidden units was set to 100, and the dropout rate was set to 0.5). Finally, in terms of intention prediction time range, according to vehicle dynamics, if the hazardous crossing intentions of pedestrians could be predicted appropriately in advance, a greater reaction time could be provided to the driver or the automated driving \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & & **Acc.** & **Rec.** & **F1.** & **Pre.** \\ \hline \multirow{3}{*}{LSTM} & Min & 0.947 & 0.945 & 0.937 & 0.951 \\  & Max & 0.978 & 0.994 & 0.994 & 0.980 \\  & Ave. & 0.967 & 0.969 & 0.971 & 0.970 \\ \hline \multirow{3}{*}{Bi-LSTM} & Min & 0.944 & 0.959 & 0.948 & 0.926 \\  & Max & 0.975 & 0.993 & 0.977 & 0.971 \\  & Ave. & 0.959 & 0.970 & 0.962 & 0.955 \\ \hline \multirow{3}{*}{RF} & Min & 0.865 & 0.812 & 0.825 & 0.838 \\  & Max & 0.916 & 0.899 & 0.882 & 0.903 \\  & Ave. & 0.890 & 0.859 & 0.859 & 0.865 \\ \hline \multirow{3}{*}{Ours} & Min & 0.963 & 0.966 & 0.960 & 0.965 \\  & Max & 0.984 & 0.994 & 0.988 & 0.985 \\ \cline{1-1}  & Ave. & **0.974** & **0.975** & **0.978** & **0.976** \\ \hline \hline \end{tabular}  
* Marked in **bold**: the best model.  
\end{table} 
Table 6: Comparison of results for various pedestrian hazardous crossing intention recognition algorithms. Recognition Accuracy (Acc. ), Recall (Rec. ), Fl-score (F1. ), Precision (Pre.). Min, Max, and Average (Ave.) results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & & **Acc.** & **Rec.** & **F1.** & **Pre.** \\ \hline \multirow{3}{*}{Position [8]} & Min & 0.662 & 0.629 & 0.747 & 0.774 \\  & Max & 0.788 & 0.825 & 0.798 & 0.920 \\  & Ave. & 0.723 & 0.713 & 0.774 & 0.864 \\ \hline \multirow{3}{*}{Position,2D pose [4]} & Min & 0.806 & 0.847 & 0.807 & 0.745 \\  & Max & 0.895 & 0.954 & 0.905 & 0.915 \\  & Ave. & 0.861 & 0.886 & 0.869 & 0.856 \\ \hline \multirow{3}{*}{Position,3D pose [45]} & Min & 0.954 & 0.960 & 0.957 & 0.949 \\  & Max & 0.972 & 0.982 & 0.974 & 0.983 \\  & Ave. & 0.962 & 0.968 & 0.965 & 0.963 \\ \hline \multirow{3}{*}{Ours} & Min & 0.963 & 0.966 & 0.960 & 0.965 \\  & Max & 0.984 & 0.994 & 0.988 & 0.985 \\ \cline{1-1}  & Ave. & **0.974** & **0.975** & **0.978** & **0.976** \\ \hline \hline \end{tabular}  
* Marked in **bold**: the best model.  
\end{table} 
Table 5: Comparison of results for various input parameters when predicting pedestrian hazardous crossing intention. Recognition Accuracy (Acc. ), Recall (Rec. ), F1-score (F1. ), Precision (Pre.). Min, Max, and Average (Ave.) results. system. According to a study by Mercedes-Benz, Germany, it was found that if the driver could be warned 0.5 s earlier, 60% of traffic accidents could be effectively avoided [47]. Therefore, to ensure safety, we advanced the prediction time to 1 s to verify the intention prediction effectiveness of our method at 0.1, 0.5, and 1 s before the pedestrians crossed the street (this was achieved by adopting [PERSON]'s method of independent variable advancement [6]). The calculation results are shown in Table 7. With the increase in the time range, although the evaluation indices all decreased, even in the case of 1 s advance, the indices were still able to be maintained above 0.88. In addition, some of the experimental results are shown in Figure 13.