Publications

Papers

1. 

   MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

   Theodor Westny, Joel Oskarsson, Björn Olofsson, and Erik Frisk

   IEEE Transactions on Intelligent Vehicles 2023

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   Enabling resilient autonomous motion planning requires robust predictions of surrounding road users’ future behavior. In response to this need and the associated challenges, we introduce our model, titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multi-modal probabilistic predictions are provided by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.

   @article{westny2023graph,
     journal = {IEEE Transactions on Intelligent Vehicles},
     title = {MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs},
     year = {2023},
     author = {Westny, Theodor and Oskarsson, Joel and Olofsson, Bj{\"o}rn and Frisk, Erik},
   }

2. 

   Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

   Theodor Westny, Joel Oskarsson, Björn Olofsson, and Erik Frisk

   In 2023 IEEE Intelligent Vehicles Symposium (IV) 2023

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   Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can greatly improve predictions.

   @inproceedings{westny2023evaluation,
     title = {Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction},
     author = {Westny, Theodor and Oskarsson, Joel and Olofsson, Bj{\"o}rn and Frisk, Erik},
     booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)},
     year = {2023},
   }

3. 

   Temporal Graph Neural Networks for Irregular Data

   Joel Oskarsson, Per Sidén, and Fredrik Lindsten

   In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics 2023

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   This paper proposes a temporal graph neural network model for forecasting of graph-structured irregularly observed time series. Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph. This is achieved by introducing a time-continuous latent state in each node, following a linear Ordinary Differential Equation (ODE) defined by the output of a Gated Recurrent Unit (GRU). The ODE has an explicit solution as a combination of exponential decay and periodic dynamics. Observations in the graph neighborhood are taken into account by integrating graph neural network layers in both the GRU state update and predictive model. The time-continuous dynamics additionally enable the model to make predictions at arbitrary time steps. We propose a loss function that leverages this and allows for training the model for forecasting over different time horizons. Experiments on simulated data and real-world data from traffic and climate modeling validate the usefulness of both the graph structure and time-continuous dynamics in settings with irregular observations.

   @inproceedings{tgnn4i,
     title = {Temporal Graph Neural Networks for Irregular Data},
     author = {Oskarsson, Joel and Sid\'en, Per and Lindsten, Fredrik},
     booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
     pages = {4515--4531},
     year = {2023},
     editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
     volume = {206},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
     video = {https://www.youtube.com/watch?v=r0mpZjUnpHA},
   }

4. 

   Scalable Deep Gaussian Markov Random Fields for General Graphs

   Joel Oskarsson, Per Sidén, and Fredrik Lindsten

   In Proceedings of the 39th International Conference on Machine Learning 2022

   Abs Bib PDF Video Code Poster Slides

   Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields (GMRFs) provides a principled way to define Gaussian models on graphs by utilizing their sparsity structure. We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only. By designing a new type of layer we enable the model to scale to large graphs. The layer is constructed to allow for efficient training using variational inference and existing software frameworks for Graph Neural Networks. For a Gaussian likelihood, close to exact Bayesian inference is available for the latent field. This allows for making predictions with accompanying uncertainty estimates. The usefulness of the proposed model is verified by experiments on a number of synthetic and real world datasets, where it compares favorably to other both Bayesian and deep learning methods.

   @inproceedings{pmlr-v162-oskarsson22a,
     title = {Scalable Deep {G}aussian {M}arkov Random Fields for General Graphs},
     author = {Oskarsson, Joel and Sid{\'e}n, Per and Lindsten, Fredrik},
     booktitle = {Proceedings of the 39th International Conference on Machine Learning},
     pages = {17117--17137},
     year = {2022},
     editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
     volume = {162},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
     video = {https://icml.cc/virtual/2022/spotlight/17282},
   }

Workshop papers

1. 

   Graph-based Neural Weather Prediction for Limited Area Modeling

   Joel Oskarsson, Tomas Landelius, and Fredrik Lindsten

   In NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning 2023

   Abs Bib PDF Code Poster

   The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.

   @inproceedings{oskarsson2023graphbased,
     title = {Graph-based Neural Weather Prediction for Limited Area Modeling},
     author = {Oskarsson, Joel and Landelius, Tomas and Lindsten, Fredrik},
     booktitle = {NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning},
     year = {2023},
   }

2. 

   Temporal Graph Neural Networks with Time-Continuous Latent States

   Joel Oskarsson, Per Sidén, and Fredrik Lindsten

   In ICML Workshop on Continuous Time Methods for Machine Learning 2022

   Note: A substantial extension of this research is presented in the paper "Temporal Graph Neural Networks for Irregular Data"

   Abs Bib PDF Video Code Poster Slides

   We propose a temporal graph neural network model for graph-structured irregular time series. The model is designed to handle both irregular time steps and partial graph observations. This is achieved by introducing a time-continuous latent state in each node of the graph. The latent dynamics are defined using a state-dependent decay-mechanism. Observations in the graph neighborhood are taken into account by integrating graph neural network layers in both the state update and predictive model. Experiments on a traffic forecasting task validate the usefulness of both the graph structure and time-continuous dynamics in this setting.

   @inproceedings{temporal_continuous_gnns,
     author = {Oskarsson, Joel and Sid{\'e}n, Per and Lindsten, Fredrik},
     title = {Temporal Graph Neural Networks with Time-Continuous Latent States},
     booktitle = {ICML Workshop on Continuous Time Methods for Machine Learning},
     year = {2022},
     video = {https://icml.cc/virtual/2022/workshop/13452#wse-detail-18688},
   }

Theses

1. 

   Probabilistic Regression using Conditional Generative Adversarial Networks

   Joel Oskarsson

   2020

   MSc Thesis

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   Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen great success in generating complex high-dimensional data, but less work has been done on their use for regression problems. This thesis presents experiments to better understand how conditional GANs can be used in probabilistic regression. Different versions of GANs are extended to the conditional case and evaluated on synthetic and real datasets. It is shown that conditional GANs can learn to estimate a wide range of different distributions and be competitive with existing probabilistic regression models.

   @mastersthesis{Oskarsson1442847,
     author = {Oskarsson, Joel},
     institution = {Linköping University, The Division of Statistics and Machine Learning},
     pages = {111},
     school = {Linköping University, The Division of Statistics and Machine Learning},
     title = {Probabilistic Regression using Conditional Generative Adversarial Networks},
     keywords = {machine learning, ml, regression, probabilistic, distribution, cgan, gan, conditional gan, adversarial networks, neural network, deep learning, f-gan, f-cgan, f-divergence, adversarial training, bimodal, heteroskedastic, mmd, maximum mean discrepancy, gmmn, generative moment matching network, conditional gmmn, ipm, kde, cgan evaluation, cgan regression, gan regression, cgan-regression, regression using gan, deep, nn, implicit, generative, conditional, model, complex noise, aleatoric, uncertainty, dctd, mdn, heteroskedastic regression, gp},
     year = {2020},
   }