SocialVAE: Human Trajectory Prediction using Timewise Latents

• URL: http://arxiv.org/abs/2203.08207v1
• Date: Tue, 15 Mar 2022 19:14:33 GMT
• Title: SocialVAE: Human Trajectory Prediction using Timewise Latents
• Authors: Pei Xu, Jean-Bernard Hayet, Ioannis Karamouzas
• Abstract summary: SocialVAE is a timewise variational autoencoder architecture that exploits posterior neural networks to perform prediction. We show that SocialVAE improves current state-of-the-art pedestrian trajectory prediction benchmarks.
• Score: 4.640835690336652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. In this paper, we propose SocialVAE, a novel approach for human trajectory prediction. The core of SocialVAE is a timewise variational autoencoder architecture that exploits stochastic recurrent neural networks to perform prediction, combined with a social attention mechanism and backward posterior approximation to allow for better extraction of pedestrian navigation strategies. We show that SocialVAE improves current state-of-the-art performance on several pedestrian trajectory prediction benchmarks, including the ETH/UCY benchmark, the Stanford Drone Dataset and SportVU NBA movement dataset. Code is available at: {\tt https://github.com/xupei0610/SocialVAE}.

Related papers

• From Cognition to Precognition: A Future-Aware Framework for Social Navigation [1.9094009409000596]
  We propose a reinforcement learning architecture, namely Falcon, to tackle socially-aware navigation. We introduce a novel SocialNav benchmark containing two new datasets, Social-HM3D and Social-MP3D. We conduct a detailed experimental analysis with the state-of-the-art learning-based method and two classic rule-based path-planning algorithms.
  arXiv  Detail & Related papers  (2024-09-20T06:08:24Z)
• Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space [3.487370856323828]
  This work introduces an integrated prediction and planning approach that incorporates the uncertainties of predicted pedestrian states in the training of a model-free DRL algorithm. A novel reward function encourages the AV to respect pedestrians' personal space, decrease speed during close approaches, and minimize the collision probability with their predicted paths. Results show a 40% decrease in collision rate and a 15% increase in minimum distance to pedestrians compared to the state of the art model that does not account for prediction uncertainty.
  arXiv  Detail & Related papers  (2024-05-22T20:09:21Z)
• Social-Transmotion: Promptable Human Trajectory Prediction [65.80068316170613]
  Social-Transmotion is a generic Transformer-based model that exploits diverse and numerous visual cues to predict human behavior. Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY.
  arXiv  Detail & Related papers  (2023-12-26T18:56:49Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion [87.77727495366702]
  We introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors.
  arXiv  Detail & Related papers  (2022-03-04T18:39:31Z)
• Probabilistic Human Motion Prediction via A Bayesian Neural Network [71.16277790708529]
  We propose a probabilistic model for human motion prediction in this paper. Our model could generate several future motions when given an observed motion sequence. We extensively validate our approach on a large scale benchmark dataset Human3.6m.
  arXiv  Detail & Related papers  (2021-07-14T09:05:33Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction [30.61190086847564]
  We propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs) Human interactions are modeled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation.
  arXiv  Detail & Related papers  (2020-05-17T17:21:23Z)
• It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction [59.027152973975575]
  We present Predicted Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant endpoints to assist in long-range multi-modal trajectory prediction. We show that PECNet improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by 20.9% and on the ETH/UCY benchmark by 40.8%.
  arXiv  Detail & Related papers  (2020-04-04T21:27:13Z)
• A Novel Graph based Trajectory Predictor with Pseudo Oracle [15.108410951760131]
  GTPPO is an encoder-decoder-based method conditioned on pedestrians' future behaviors. It is evaluated on ETH, UCY and Stanford Drone datasets.
  arXiv  Detail & Related papers  (2020-02-02T13:40:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.