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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
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A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
TL;DR: This paper attempts to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications, and compares the commonalities and differences of these GAns methods.
Proceedings ArticleDOI
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings
TL;DR: A probabilistic forecasting model of future interactions between a variable number of agents that performs both standard forecasting and the novel task of conditional forecasting, which reasons about how all agents will likely respond to the goal of a controlled agent.
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Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
TL;DR: Trajectron++ is a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data and outperforming a wide array of state-of-the-art deterministic and generative methods.
Journal ArticleDOI
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
TL;DR: A comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction based on three criteria: input representation, output type, and prediction method is provided.
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VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
TL;DR: VectorNet is introduced, a hierarchical graph neural network that first exploits the spatial locality of individual road components represented by vectors and then models the high-order interactions among all components and obtains state-of-the-art performance on the Argoverse dataset.
References
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
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.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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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.