Space-Time Tree Search for Long-Term Trajectory Prediction

Abstract

The pedestrian trajectory prediction is critical for autonomous driving, intelligent navigation, and abnormal behavior detection. With the booming of artificial intelligence (AI), many researchers have employed deep learning technologies to solve the pedestrian trajectory prediction problem and obtained relatively better performance in the short-term trajectory prediction. However, long-term trajectory prediction is still challenging to achieve high prediction accuracy. In this work, we propose a space-time tree search (STTS) method for long-term pedestrian trajectory prediction. Compared with existing methods only considering the problem from the space dimension, the proposed method formulates the trajectory prediction problem as a joint space-time tree search process by mapping the environment to a grid map. Since the human's trajectory is relative to space and time dimensions, the trajectory prediction accuracy can be improved by the two dimensions. Then, a space-time reward trained neural network is employed to extract the pedestrian's intent with both the scene image and the historical trajectory as input and outputs the prior search probabilities. Finally, the tree search can obtain the optimal predicted trajectory according to the prior probabilities, significantly improving the tree search efficiency. After testing, our proposed method can perform better than existing methods.