Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting

• URL: http://arxiv.org/abs/2209.12243v1
• Date: Sun, 25 Sep 2022 15:18:56 GMT
• Title: Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting
• Authors: Parth Kothari and Alexandre Alahi
• Abstract summary: Human forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. We introduce SGANv2, an improved safety-compliant SGAN architecture equipped with motion-temporal interaction modelling and a transformer-based discriminator design.
• Score: 95.82600221180415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN-based designs to better model human motion in crowds. Despite superior performance in reducing distance-based metrics, current networks fail to output socially acceptable trajectories, as evidenced by high collisions in model predictions. To counter this, we introduce SGANv2: an improved safety-compliant SGAN architecture equipped with spatio-temporal interaction modelling and a transformer-based discriminator. The spatio-temporal modelling ability helps to learn the human social interactions better while the transformer-based discriminator design improves temporal sequence modelling. Additionally, SGANv2 utilizes the learned discriminator even at test-time via a collaborative sampling strategy that not only refines the colliding trajectories but also prevents mode collapse, a common phenomenon in GAN training. Through extensive experimentation on multiple real-world and synthetic datasets, we demonstrate the efficacy of SGANv2 to provide socially-compliant multimodal trajectories.

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