IAT: Pedestrian Intention and Trajectory Prediction

Abstract

With the continuous advancement of science and technology, the artificial intelligence industry is developing more and more rapidly, and the research field focusing on autonomous driving continues to attract young engineers to join in. In the field of autonomous driving, the estimation of pedestrian intention at urban traffic intersections is a very important part of the entire field, and the technical difficulties and challenges faced are undoubtedly the biggest, because the life safety of pedestrians is involved here. Pedestrian trajectory prediction is also a very challenging topic in the field of autonomous driving or assisted driving. In this article, we design a splicing network that combines pedestrian intention and trajectory to build a set of network models that predict pedestrian intention and pedestrian trajectory. We call this model "IAT" (intention and trajectory). We train the collected data and test it on the validation set. The accuracy is 95%. The overlap of the trajectory and the walking trajectory of the human in reality almost overlap within 2s.