CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN

IEEE Access

Tianlu Mao¹  Huikun Bi¹  Haibo Liu²  Shaohua Liu² 

[1] Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China;

关键词: Trajectory prediction;    generative adversarial network;    deep learning;   

DOI  :  10.1109/ACCESS.2020.2987072

来源: DOAJ

【 摘 要 】

Predicting plausible and collisionless trajectories is critical in various applications, such as robotic navigation and autonomous driving. This is a challenging task due to two major factors. First, it is difficult for deep neural networks to understand how pedestrians move to avoid collisions and how they react to each other. Second, given observed trajectories, there are multiple possible and plausible trajectories followed by pedestrians. Although an increasing number of previous works have focused on modeling social interactions and multimodality, the trajectories generated by these methods still lead to many collisions. In this work, we propose CoL-GAN, a new attention-based generative adversarial network using a convolutional neural network as a discriminator, which is able to generate trajectories with fewer collisions. Through experimental comparisons with prior works on publicly available datasets, we demonstrate that Col-GAN achieves state-of-the-art performance in terms of accuracy and collision avoidance.

【 授权许可】

Unknown