## 4 Experiments
### Pedestrian Hazardous Crossing Intention Recognition and Prediction Experiment  
First, in terms of the types of pedestrian crossing intention recognition or prediction input variables, as shown in Table 5, we chose three control groups to be classified using our network separately to illustrate the validity of our selected input parameters. For the first control group, we compared the results for the proposed input variables with the results obtained with the input used by [PERSON] et al.--only the historical location of the pedestrian--for performing pedestrian trajectory prediction [8]. For the second control group, we compared results for the proposed input variables with the results obtained with the inputs used by [PERSON] et al.--the historical location of the pedestrian and the 2D pose estimate--for performing pedestrian intention recognition [4]. For the third control group, we compared the results for the proposed input variables with the results obtained with the inputs used by [PERSON] et al.--the trajectory prediction using the pedestrian's historical position and the 3D pose estimate--as input variables for comparison [46]. Finally, we selected the 3D pose features, interference state features, pedestrian blind observation state features, and pedestrian speed features as input variables for comparison. The computational results with our selected input parameters were improved for all evaluation metrics compared to the input variables used for the control groups. Second, in terms of the pedestrian crossing intention recognition computational methods, considering that the pedestrian hazardous crossing intention prediction module was obtained based on an LSTM improvement in this paper, we choose an LSTM and its improved Bi-LSTM as two algorithms for comparison. In addition, [PERSON] et al. [6] used the SVM, RF, gradient boosting machine (GBM), and extreme gradient boosting machine (XGBM) for pedestrian crossing intention recognition in their study, and the accuracy of the RF algorithm was higher than those of the other algorithms. Therefore, we chose the RF algorithm as another algorithm control group. 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). Figure 12: Schematic diagram of pedestrian blind spot observation status classification. N.O. denotes “No Observation”, meaning the pedestrian did not observe the traffic condition in the blind zone. P.O. denotes “Proper Observation”, meaning the pedestrian observed the traffic condition in the blind zone, and W.O. denotes “Wrong Observation”, meaning the pedestrian’s observation direction was opposite to the position of the blind zone. Second, in terms of the pedestrian crossing intention recognition computational methods, considering that the pedestrian hazardous crossing intention prediction module was obtained based on an LSTM improvement in this paper, we choose an LSTM and its improved Bi-LSTM as two algorithms for comparison. In addition, [PERSON] et al. [6] used the SVM, RF, gradient boosting machine (GBM), and extreme gradient boosting machine (XGBM) for pedestrian crossing intention recognition in their study, and the accuracy of the RF algorithm was higher than those of the other algorithms. Therefore, we choose the RF algorithm as another algorithm control group.