Causal Temporal–Spatial Pedestrian Trajectory Prediction With Goal Point Estimation and Contextual Interaction

Forecasting pedestrian trajectories in complex dynamic environments is highly critical for the application of autonomous vehicles and robots. Accordingly, this paper proposes a novel pedestrian trajectory prediction model called CTSGI, which, utilizes self-attention mechanism to construct an interactive graph between pedestrians and their neighbors based on their spatial relationship, to model the crowd’s interaction. At the same time, it uses self-attention to extract the temporal dependence for a single pedestrian. In order to effectively model the interaction between the pedestrian and the context, such as where a pedestrian can walk or approach, the semantic segmentation of background image is utilized. CTSGI estimates the goal points of pedestrian and their neighbors to assist in predicting the future trajectory. In addition, the causal structure model is used to analyze the confounding factors existing in the encoding stage, and Do-calculus is introduced for eliminating the confounding impact to improve the prediction performance. Moreover, extensive experiments are conducted for the proposed model on ETH and UCY datasets, and clearly, the experimental results reveal that the model reported herein outperforms the comparative state-of-the-art methods by 27.59% in Average Displacement Error (ADE) and 8.33% in Final Displacement Error (FDE). Furthermore, visualization of attention indicates that our model can capture the interaction between some specific pedestrian and their neighbors better.