## 3 Methodology
### Pedestrian Feature Modules
#### 3.3.3 Pedestrian Observation State Feature Module  
When the pedestrian view is obscured, the pedestrian's observation level (including the observation direction and duration) of the traffic condition in the blind area directly affects the pedestrian's final crossing intention decision. In order to observe the environment in the obstructed field of view, pedestrians not only need to turn their heads and shoulders but also sometimes need to add a forward tilt of the limb trunk to complete the observation. Thus, we also added the forward tilt angle \(\gamma\) of the limb trunk, as shown in Figure 4b.  
Let the point \(Midhip(x_{8},y_{8},z_{8})_{t}\) be the coordinates of the midpoint of the line connecting the left hip and the right hip at the moment \(t\) (mid-hip point). Based on the side view of the human skeletal points in Figure 4b, the forward tilt angle \(\gamma t\) of the trunk at the moment \(t\) is  
\[\gamma_{t}=\arcsin\left(\frac{\sqrt{(z_{1}-z_{8})^{2}}}{\sqrt{(z_{1}-z_{8})^{ 2}+(x_{1}-x_{8})^{2}}}\right) \tag{22}\]  
Let the pedestrian blind zone observation vector at time \(t\) be \(\mathbf{O}(\alpha_{t},\beta_{t},\gamma_{t})\), where \(\alpha\)t, \(\beta\)t, and \(\gamma t\) are the head steering angle, shoulder steering angle, and torso forward angle of the pedestrian at time t. After obtaining the pedestrian blind zone observation vector, in order to be able to judge whether the pedestrian is observing the road condition in the blind zone at moment t based on the pedestrian blind zone observation vector at moment t, we designed a pedestrian blind zone observation experiment (for more detailed information, refer to Experimental Procedure (1)).  
First, by collecting the head steering angle, shoulder steering angle, and torso forward angle from different experimenters while observing the road in a blind area, we constructed a pedestrian blind area observation degree body pose dataset, the PO Dataset, \(\{(\alpha_{1},\beta_{1},\gamma_{1}),\cdots\}\).  
Figure 6: Schematic diagram of pedestrian 3D pose feature selection: (**a**) Openpose-Body25 model and (**b**) body joint classification methods.  
Then, the K-means++ algorithm was used to obtain the pose clustering centroids \(C_{unaware}(\alpha_{uw},\beta_{uw},\gamma_{uw})\) when the pedestrian was not observing the road conditions and the pose clustering centroids \(C_{aware\_1}(\alpha_{wl},\beta_{wl},\gamma_{wl})\) and \(C_{aware\_1}(\alpha_{wr},\beta_{wr},\gamma_{wr})\) when the pedestrian was observing the left and right sides within the blind area of the visual field, respectively [43].  
Finally, for a new image, the observation state was determined based on the cluster that it was closest to. This was determined by the minimum Euclidean distance, \(\min(\rho_{un},\rho_{wr},\rho_{wl})\), where \(\rho_{un}=\sqrt{(\alpha_{t}-\alpha_{uw})^{2}+(\beta_{t}-\beta_{uw})^{2}+( \gamma_{t}-\gamma_{uw})^{2}}\). \(\rho_{wr}\) and \(\rho_{wl}\) were calculated similarly.  
The observation result was set as \(\mathbf{Observe}_{t}^{i}\), which took values of 0, 1, or 2, corresponding to correctly observed, unobserved (forward observation), and incorrectly observed (according to Chinese traffic regulations, vehicles drive on the right side of the road and the direction of incoming traffic is to the left of the pedestrian; thus, a pedestrian who only observes to the right is defined as incorrectly observed), respectively.