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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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Proceedings ArticleDOI
Video-Based Activity Forecasting for Construction Safety Monitoring Use Cases
Proceedings ArticleDOI
A Multi-Vehicle Trajectories Generator to Simulate Vehicle-to-Vehicle Encountering Scenarios
TL;DR: Comparison of the proposed MTG with $\beta$-VAE and InfoGAN demonstrates that the MTG has stronger capability to purposely generate rational vehicle-to-vehicle encounters through operating the disentangled latent codes.
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Following Social Groups: Socially Compliant Autonomous Navigation in Dense Crowds
Xinjie Yao,Ji Zhang,Jean Oh +2 more
TL;DR: The underlying system incorporates a deep neural network to track social groups and join the flow of a social group in facilitating the navigation, and is capable of navigating safely in a densely populated area following crowd flows to reach the goal.
Journal ArticleDOI
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
Adithya Ranga,Filippo Giruzzi,Jagdish Bhanushali,Emilie Wirbel,Patrick Pérez,Tuan-Hung Vu,Xavier Perrotton +6 more
TL;DR: A multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences is proposed and experimental results show state-of-the-art performance on JAAD dataset.
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Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting.
TL;DR: This work studies the problem of future prediction at the level of 3D scenes, represented by point clouds captured by a LiDAR sensor, and presents a new object trajectory forecasting pipeline leveraging SPCSFNet, able to outperform the conventional pipeline using state-of-the-art trajectory forecasting methods.
References
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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.