time-series-9

Trajectory Modeling

3260 papers • 126 benchmarks • 313 datasets

The equivalent of language modeling but for trajectories.

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Most implemented papers

SIND: A Drone Dataset at Signalized Intersection in China

Yanchao Xu, Wenbo Shao, Jun Li, Kai-Bo Yang, Weida Wang, Huayi Huang, Chen Lv, Hong Wang•Mon Sep 05 2022

A pipeline is designed to construct a Signalized INtersection Dataset (SIND), which contains 7 hours of recording including over 13,000 TPs with 7 types and it is believed that SIND would be an effective supplement to existing datasets and can promote related research on autonomous driving.

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Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction

Samira Ebrahimi Kahou, C. Pal, Felix Heide, Felipe Codevilla, Martin Weiss, Florian Golemo, Roger Girgis, Jim Aldon D’Souza•Thu Feb 18 2021

Latent Variable Sequential Set Transformers are encoder-decoder architectures that generate scene-consistent multi-agent trajectories that achieve top results on the global nuScenes vehicle motion prediction leaderboard, and produces strong results onThe Argoverse vehicle prediction challenge.

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Q-value Regularized Transformer for Offline Reinforcement Learning

Dacheng Tao, Chaoqin Huang, Shengchao Hu, Ziqing Fan, Li Shen, Ya Zhang, Yanfeng Wang•Sun May 26 2024

The Q-value regularized Transformer (QT), which combines the trajectory modeling ability of the Transformer with the predictability of optimal future returns from DP methods to enhance the state-of-the-art in offline RL.

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The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs

M. Pavone, B. Ivanovic•Sat Oct 13 2018

Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). It combines tools from recurrent sequence modeling and variational deep generative modeling to produce a distribution of future trajectories for each agent in a scene. We demonstrate the performance of our model on several datasets, obtaining state-of-the-art results on standard trajectory prediction metrics as well as introducing a new metric for comparing models that output distributions.

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Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping

Sanjukta Krishnagopal•Thu May 28 2020

This work presents a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson’s subtypes based on disease trajectory, which can effectively assist in targeted subtype-specific treatment in the field of personalized medicine.

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baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents

A. Nguyen, Michael A. Alcorn•Fri Apr 23 2021

In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (e.g., the play a team is executing in a game of basketball). As a result, the trajectories of the agents are often statistically dependent at any given time step; however, almost universally, multi-agent models implicitly assume the agents' trajectories are statistically independent at each time step. In this paper, we introduce baller2vec++, a multi-entity Transformer that can effectively model coordinated agents. Specifically, baller2vec++ applies a specially designed self-attention mask to a mixture of location and"look-ahead"trajectory sequences to learn the distributions of statistically dependent agent trajectories. We show that, unlike baller2vec (baller2vec++'s predecessor), baller2vec++ can learn to emulate the behavior of perfectly coordinated agents in a simulated toy dataset. Additionally, when modeling the trajectories of professional basketball players, baller2vec++ outperforms baller2vec by a wide margin.

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Clustering of longitudinal data: A tutorial on a variety of approaches

N. D. Teuling, S. Pauws, E. Heuvel•Tue Nov 09 2021

A selection of methods that take different approaches to clustering longitudinal data, based on cross‐sectional clustering, distance‐based clustering, feature‐based clustering, feature‐based clustering, and mixture modeling are presented.

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Contrastive Trajectory Similarity Learning with Dual-Feature Attention

Jianzhong Qi, Yuxuan Liang, Yanchuan Chang, E. Tanin•Mon Oct 10 2022

This work proposes a contrastive learning-based trajectory modeling method named TrajCL, which can jointly learn both the spatial and the structural patterns of trajectories and can outperform the state-of-the-art supervised method by up to 56% in the accuracy for processing trajectory similarity queries.

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Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease

A. Sinitca, A. Lyanova, Mikhail Bogachev, K. Grigarevichius, N. Pyko, M. Tsygankova, Eldar Davletshin, K. Petrov, Tatyana Ageeva, S. A. Pyko, Dmitry I. Kaplun, A. Kayumov, Y. Mukhamedshina•Wed Feb 01 2023

A marker-free instrumental approach to the analysis of gait disturbances in animal models shows that in a mouse model representative movement patterns are characterized by two asymptotic regimes characterized by integrated 1/f noise at small scales and nearly random displacements at large scales separated by a single crossover.

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