S-CGRU: an Efficient Model for Pedestrian Trajectory Prediction

EasyChair Preprint 10798

Abstract

In the development of autonomous driving systems, pedestrian trajectory prediction plays a crucial role. Existing models still face some challenges in capturing the accuracy of complex pedestrian actions in different environments and in handling large-scale data and real-time prediction efficiency. To address this, we have designed a novel Complex Gated Recurrent Unit (CGRU) model, cleverly combining the spatial expressiveness of complex numbers with the efficiency of Gated Recurrent Unit networks to establish a lightweight model. Moreover, we have incorporated a social force model to further develop a Social Complex Gated Recurrent Unit (S-CGRU) model specifically for predicting pedestrian trajectories. To improve computational efficiency, we conducted an in-depth study of the pedestrian's attention field of view in different environments to optimize the amount of information processed and increase training efficiency. Experimental verification on six public datasets confirms that S-CGRU model significantly outperforms other baseline models not only in prediction accuracy but also in computational efficiency, validating the practical value of our model in pedestrian trajectory prediction.

Keyphrases: Autonomous driving., Complex number Neural Network., Gated Recurrent Unit., Pedestrian Trajectory Prediction.

@booklet{EasyChair:10798,
  author    = {Zhenwei Xu and Qing Yu and Wushouer Slamu and Yaoyong Zhou and Zhida Liu},
  title     = {S-CGRU: an Efficient Model for Pedestrian Trajectory Prediction},
  howpublished = {EasyChair Preprint 10798},
  year      = {EasyChair, 2023}}