###### Abstract  
Pedestrians who suddenly cross the street from within the blind spot of a vehicle's field of view can pose a significant threat to traffic safety. The dangerous pedestrian crossing intentions in view-obscured scenarios have not received as much attention as the prediction of pedestrian crossing intentions. In this paper, we present a method for recognizing and predicting the dangerous crossing intention of pedestrians in a view-obscured region based on the interference, pose, velocity observation-long short-term memory (IPVO-LSTM) algorithm from a road-based view. In the first step, the road-based camera captures the pedestrian's image. Then, we construct a pedestrian interference state feature module, pedestrian three-dimensional pose feature module, pedestrian velocity feature module, and pedestrian blind observation state feature module and extract the corresponding features of the studied pedestrians. Finally, the pedestrian hazard crossing intention prediction module based on a feature-fused LSTM (ff-LSTM) and attention mechanism is used to fuse and process the above features in a cell state process to recognize and predict the pedestrian hazard crossing intention in the blind visual area. Experiments are compared with current common algorithms in terms of the input parameter selection, intention recognition algorithm, and intention prediction time range, and the experimental results validate our state-of-the-art method. pedestrian crossing intention; pose estimation; infrastructure-to-vehicle (I2V) 202320233323  
crossing intentions in view-obscured scenarios. Although this problem occurs in a more specific traffic situation, the solution is significant for improving active vehicle safety and fully promoting the commercial application of L5-level autonomous driving systems. In this paper, based on a depth camera, pose estimation technique, and spatial-temporal graph convolutional networks (ST-GCN), we first obtain pedestrian three-dimensional (3D) pose feature points and pedestrian action types, and we then construct a pedestrian interference state feature module, pedestrian 3D pose feature module, pedestrian speed feature module, and pedestrian blind observation state feature module. We extract the pedestrian features based on these modules. These features are concatenated in the cell state process of our constructed ff-LSTM, and the pedestrian hazard crossing intention in the blind area of the visual field is finally recognized and predicted by the pedestrian hazardous crossing intention prediction module. With the popularization and development of infrastructure-to-vehicle (I2V) communication, the model proposed in this paper can compensate for the lack of perception ability of autonomous driving systems and conventional vehicles in view-obscured environments to improve the decision reliability and safety of autonomous driving systems and drivers. Our contributions are summarized as follows:  
1. For the issue of recognizing and predicting pedestrians' hazardous crossing intentions in view-obscured areas, we present the interference, pose, velocity observation-long short-term memory (IPVO-LSTM) algorithm based on the road-based view. 2. When selecting the input parameters for the pedestrian hazardous crossing intention recognition and prediction, we added interference features between pedestrians and blind observation degree features to the pedestrian pose features and historical trajectory features and constructed an ff-LSTM for fusing the above features. 3. When performing interference judgment of pedestrians, we added the pedestrian action type and body pose features to the pedestrian spatial location features. 4. In extracting the pedestrian blind observation degree features, we proposed a method to determine the pedestrian blind observation status based on the pedestrian head pose angle, torso forward angle, shoulder pose angle, and K-Means++ algorithm. The remainder of this paper is organized as follows. Section 2 reviews the related work.