Proof and experiments show the MoN loss approximates the square root of the ground truth PDF in probabilistic trajectory prediction instead of the PDF.
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Social-BiGAT is a graph-based generative adversarial network using GAT for social interaction features and Bicycle-GAN for multimodal outputs that reports state-of-the-art results on pedestrian trajectory forecasting benchmarks.
citing papers explorer
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Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction
Proof and experiments show the MoN loss approximates the square root of the ground truth PDF in probabilistic trajectory prediction instead of the PDF.
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Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
Social-BiGAT is a graph-based generative adversarial network using GAT for social interaction features and Bicycle-GAN for multimodal outputs that reports state-of-the-art results on pedestrian trajectory forecasting benchmarks.