论文标题
符合安全性的生成对抗网络，用于人类轨迹预测
Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting
论文作者
论文摘要
人群中的人类轨迹预测提出了建模社交相互作用和输出无碰撞多模式分布的挑战。随着社会生成对抗网络（SGAN）的成功，最近的作品提出了各种基于GAN的设计，以更好地模拟人群中的人类运动。尽管在减少基于距离的指标方面的性能卓越，但当前网络仍无法输出社会可接受的轨迹，这在模型预测中的高碰撞中证明了这一点。为了解决这个问题，我们介绍了SGANV2：改进的符合安全性的SGAN架构，配备了时空相互作用建模和基于变压器的鉴别器。时空建模能力有助于更好地学习人类的社交互动，而基于变压器的歧视器设计改善了时间序列建模。此外，SGANV2即使在测试时间通过协作抽样策略也利用了学到的判别器，该策略不仅完善了碰撞轨迹，还可以防止模式崩溃，这是GAN训练中的常见现象。通过对多个现实世界和合成数据集进行广泛的实验，我们证明了SGANV2提供了符合社会符合社会的多模式轨迹的功效。
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.
