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Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks.
TLDR
In this paper, a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people, and predicts socially plausible future by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss.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.read more
Citations
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Book ChapterDOI
DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
Ye Yuan,Kris M. Kitani +1 more
TL;DR: In this paper, the authors proposed a sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model.
Journal ArticleDOI
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Fanta Camara,Nicola Bellotto,Serhan Cosar,Florian Weber,Dimitris Nathanael,Matthias Althoff,Jingyuan Wu,Johannes Ruenz,André Dietrich,Gustav Markkula,Anna Schieben,Fabio Tango,Natasha Merat,Charles Fox +13 more
TL;DR: This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified.
Book ChapterDOI
RED: A Simple but Effective Baseline Predictor for the TrajNet Benchmark
TL;DR: It is shown how a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve top-rank at the TrajNet 2018 challenge compared to elaborated models.
Posted Content
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
TL;DR: This paper proposes a generic trajectory forecasting framework with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents and introduces a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance.
Journal ArticleDOI
AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction
TL;DR: Experimental results on two benchmark pedestrian trajectory prediction datasets demonstrate the competitive performances of the proposed method in terms of both the final displace error and the average displacement error metrics as compared with state-of-the-art trajectory prediction methods.
References
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Posted Content
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Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.