Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

Abstract

Predicting the future trajectories of multiple interacting agents in a scene has become an increasingly important problem for many different applications ranging from control of autonomous vehicles and social robots to security and surveillance. This problem is compounded by the presence of social interactions between humans and their physical interactions with the scene. While the existing literature has explored some of these cues, they mainly ignored the multimodal nature of each human's future trajectory. In this paper, we present Social-BiGAT, a graph-based generative adversarial network that generates realistic, multimodal trajectory predictions by better modelling the social interactions of pedestrians in a scene. Our method is based on a graph attention network (GAT) that learns reliable feature representations that encode the social interactions between humans in the scene, and a recurrent encoder-decoder architecture that is trained adversarially to predict, based on the features, the humans' paths. We explicitly account for the multimodal nature of the prediction problem by forming a reversible transformation between each scene and its latent noise vector, as in Bicycle-GAN. We show that our framework achieves state-of-the-art performance comparing it to several baselines on existing trajectory forecasting benchmarks.

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Cited by in corpus (10)

• MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
• Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
• Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene
• Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
• TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction
• HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
• AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction
• LTN: Long-Term Network for Long-Term Motion Prediction
• Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories