Abstract: Accurate and efficient pedestrian trajectory prediction is one of the key capabilities for the safe operation of self-driving vehicles. Therefore, it is of great significance to study pedestrian trajectory prediction algorithms applicable to complex interaction scenarios. In this study, a spatial gridding-based multi-attention generative adversarial network (SGMA-GAN) is proposed, which is modeled with generative adversarial network as the main framework. Firstly, the map information is gridded to better represent the pedestrian state information in tensor form, improve the stability of the state space and network structure. Secondly, temporal and spatial attention mechanisms are introduced to account for the effects of historical trajectories and spatial interaction features. Finally, the model is evaluated with both Eidgen?ssische Technische Hochschule (ETH) and University of Cyprus (UCY) datasets. The results showed that as the prediction step size gradually increased, compared with the relatively new SGANv2, the mean average displacement error (ADE) and Final displacement error (FDE) of SGMA-GAN in five scenarios increased by 10.61% and 4.65%, respectively.