## 2 Related Work
### Observational Perspective for Pedestrian Crossing Intentions Prediction  
By watching a large number of road traffic accident videos, we found that when pedestrians' vision is obstructed, pedestrians do not always effectively observe the road conditions in the blind spot before crossing the street, often looking in the wrong direction. Failure to observe the road conditions can lead to pedestrians directly entering the vehicle driving area, causing accidents. As shown in Table 1, the previous research on vision-based pedestrian intention or trajectory prediction was mainly from two observation perspectives: on-board and road-based vision sensors. Most studies based on on-board vision sensors have focused on pedestrians directly in front of the sensor's field of view. For example, [PERSON] et al. [7] predicted the pedestrian trajectory ahead of the vehicle based on the image information captured by on-board vision sensors and an improved LSTM model. [PERSON] et al. [4] implemented cyclist and pedestrian intention prediction in front of the field of view based on image information captured by on-board vision sensors and a convolutional neural network (CNN) + RF model. However, it is clear that the on-board sensor-based approach cannot overcome the critical problem of field of view occlusion and cannot meet the needs of the pedestrian intention or trajectory prediction in an area where the field of view is occluded. In fact, self-vehicle-sensor field-of-view occlusion is also an important cause of difficulties for autonomous driving systems to make safe decisions and produce human-like driving behavior in dynamic and uncertain environments [19]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Title** & **Method** & **Input Data** & **Observe Orientation** & **View Obscured** \\ \hline [PERSON] et al. [4], 2019 & CNN + RF & 2D pose estimation & On Board & No \\ [PERSON] et al. [5], 2018 & CNN + LSTM & Trajectory, relative position, scene image features & Bird’s-eye view & No \\  & & 2D Pose estimation, temperature, traffic light & Bird’s-eye view & No \\ [PERSON] et al. [6], 2021 & SVM, RF, GBM, XGBT & duration, and others & & \\ [PERSON] et al. [7], 2021 & Holistic LSTM & Trajectory, speed, and intent assessment & On board & No \\ [PERSON] et al. [8], 2016 & CNN + LSTM & Trajectory & On board & No \\  & & Pedestrian pose 3D & On board & No \\ [PERSON] et al. [9], 2020 & CNN & key points & On board & No \\ [PERSON] et al. [10], 2021 & HSTA & Trajectory & Bird’s-eye view & No \\ [PERSON] et al. [11], 2021 & LSTM & Pedestrian trajectory and & Bird’s-eye view & No \\ [PERSON] et al. [12], 2021 & CNN & Trajectory & On board & No \\ [PERSON] et al. [13] 2021 & GCN + LSTM & Pose estimation and object & On board & No \\  & & visual features & & \\ [PERSON] et al. [14], 2021 & LSTM & Coordinates and directions & On board & No \\ [PERSON] et al. [15], 2020 & BiLSTM + CNN + GCN & Trajectory & Bird’s-eye view & No \\ [PERSON] et al. [16], 2017 & Multi-player & Age attributes, speed, and body direction & Bird’s-eye view & No \\ [PERSON] et al. [17], 2019 & Spatio-temporal & Sequential frame images & On board & No \\ [PERSON] et al. [18], 2019 & KF, ML & Trajectory, motion state & Bird’s-eye view & No \\ \hline \end{tabular}
\end{table} 
Table 1: Literature on pedestrian intention prediction. Compared to on-board vision sensors, a road-based sensor-based view can provide a complete field of view. [PERSON] et al. [6] captured videos of pedestrians crossing a road through a road-based camera and analyzed the intentions of pedestrians running red lights to cross the road at traffic light intersections based on information such as pedestrian two-dimensional (2D) skeletal points. [PERSON] et al. [20] developed a human-in-the-loop system based on lidar mounted on the roadside to bring pedestrians into the connected environment and predict pedestrian crossing intentions using an improved Naive Bayes method.