Complementary Attention Gated Network for Pedestrian Trajectory Prediction


  • Jinghai Duan Xi'an Jiaotong University
  • Le Wang Xi'an Jiaotong University
  • Chengjiang Long JD Finance America Corporation
  • Sanping Zhou Xi'an Jiaotong University
  • Fang Zheng Xi’an Jiaotong University
  • Liushuai Shi Xi’an Jiaotong University
  • Gang Hua Wormpex AI Research



Computer Vision (CV)


Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories and social environments, different pedestrians may make completely different future decisions. However, most existing methods only focus on the frequent modal of the trajectory and thus are difficult to generalize to the peculiar scenario, which leads to the decline of the multimodal fitting ability when facing similar scenarios. In this paper, we propose a complementary attention gated network (CAGN) for pedestrian trajectory prediction, in which a dual-path architecture including normal and inverse attention is proposed to capture both frequent and peculiar modals in spatial and temporal patterns, respectively. Specifically, a complementary block is proposed to guide normal and inverse attention, which are then be summed with learnable weights to get attention features by a gated network. Finally, multiple trajectory distributions are estimated based on the fused spatio-temporal attention features due to the multimodality of future trajectory. Experimental results on benchmark datasets, i.e., the ETH, and the UCY, demonstrate that our method outperforms state-of-the-art methods by 13.8% in Average Displacement Error (ADE) and 10.4% in Final Displacement Error (FDE). Code will be available at




How to Cite

Duan, J., Wang, L., Long, C., Zhou, S., Zheng, F., Shi, L., & Hua, G. (2022). Complementary Attention Gated Network for Pedestrian Trajectory Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 542-550.



AAAI Technical Track on Computer Vision I