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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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Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians.
TL;DR: This work presents a novel trajectory prediction algorithm for pedestrians based on a personality-aware probabilistic feature map that improves the accuracy by 5-9% over prior algorithms.
Proceedings ArticleDOI
Trajectory Prediction based on Constraints of Vehicle Kinematics and Social Interaction
TL;DR: This work proposes a model that predicts the possible and feasible trajectory for host vehicle in 3 seconds, and introduces a prediction diagnosis method to check the continuous heading and maximum acceleration condition, pruning and adjusting the prediction candidates.
Book ChapterDOI
An Attention-Based Interaction-Aware Spatio-Temporal Graph Neural Network for Trajectory Prediction
TL;DR: Experimental results reveal the competitive performances of AST-GNN in terms of both the final displace error (FDE) and average displacement error (ADE) as compared with state-of-the-art trajectory prediction methods.
Proceedings ArticleDOI
Evaluation of Output Representations in Neural Network-based Trajectory Predictions Systems
TL;DR: It is shown that those representations working on residuals (in particular, displacements with respect of last pedestrian position or linear regression models of residual errors) produce much more accurate predictions than those ones handling absolute coordinates.
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Drowned out by the noise: Evidence for Tracking-free Motion Prediction.
TL;DR: In this article, the authors systematically explore the importance of the tracking module for the motion prediction task and conclude that tracking module is detrimental to overall motion prediction performance when the module is imperfect (with as low as 1% error).
References
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Proceedings Article
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.