Theodor Westny

Ph.D.

Autonomous Driving

Theodor Westny, Ph.D., is a Senior Engineer at Qualcomm, working on end-to-end autonomous driving systems, with a focus on learning-based planning and decision-making.

His work spans perception, world modeling, and planning, including the development of end-to-end models that integrate scene understanding with control. He is particularly interested in multimodal and generative approaches, and how foundation models can enable scalable and generalizable autonomous driving systems.

He received his doctoral degree in January 2025 from Linköping University, Sweden, with his thesis, “Context-Aware Behavior Prediction for Autonomous Driving”, which investigated multimodal trajectory prediction using deep probabilistic models and graph-based representations. His research broadly focused on AI and deep learning for autonomous systems, including probabilistic and generative modeling, continuous-time dynamics, and vehicle dynamics modeling. In addition, he has worked on data-driven fault diagnosis and prognostics for industrial systems.

He joined the Division of Vehicular Systems at Linköping University in August 2020 after obtaining his MSc. in Electrical Engineering, with a thesis on “Modeling and Lateral Control of Tractor-Trailer Vehicles during Aggressive Maneuvers”. During his studies, he completed an exchange semester at UNSW, Sydney, Australia, and several internships in software engineering and autonomous driving, including at Embark Trucks, San Francisco, USA.

Since 2018, he has been a member of the global talent network Nova.

Research Interests

Autonomous Driving
- End-to-end learning and decision-making
- Vision-Language-Action (VLA) models
- Motion planning and control
- World modeling and scene prediction
Probabilistic and Generative Modeling
- Diffusion models
- Variational Autoencoders
- Mixture density networks
- Multimodal prediction
Spatio-Temporal Modeling
- Multi-agent systems
- Graph-based learning (GNNs)
- Continuous-time models (Neural ODEs)

News

Aug 16, 2025	I’ve joined Qualcomm as a Senior Engineer, currently working with E2E motion planning for autonomous driving!
Jan 10, 2025	I am happy to share that I successfully defended my Ph.D. thesis. Many thanks to everyone involved!
May 02, 2024	Our paper, Stability-Informed Initialization of Neural Ordinary Differential Equations has been accepted to ICML 2024! Looking forward to sharing our findings in Vienna this July.
Mar 15, 2024	We are up and running! Welcome to my page

Selected Publications

Toward Unified Practices in Trajectory Prediction Research on Bird’s-Eye-View Datasets

Theodor Westny, Björn Oskarsson , and Erik Frisk

In Intelligent Vehicles Symposium (IV) , 2025

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The availability of high-quality datasets is crucial for developing behavior prediction algorithms in autonomous vehicles. This paper highlights the need to standardize the use of certain datasets for motion forecasting research to simplify comparative analysis and proposes a set of tools and practices to achieve this. Drawing on extensive experience and a comprehensive review of current literature, we summarize our proposals for preprocessing, visualization, and evaluation in the form of an open-sourced toolbox designed for researchers working on trajectory prediction problems. The clear specification of necessary preprocessing steps and evaluation metrics is intended to alleviate development efforts and facilitate the comparison of results across different studies. The toolbox is available at: https://github.com/westny/dronalize.
@inproceedings{westny2025dronalize, title = {Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasets}, author = {Westny, Theodor and Oskarsson, Bj{\"o}rn and Frisk, Erik}, booktitle = {Intelligent Vehicles Symposium (IV)}, year = {2025}, organization = {IEEE}, }
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}, volume = {8}, number = {9}, pages = {4223-4236}, author = {Westny, Theodor and Oskarsson, Joel and Olofsson, Bj{\"o}rn and Frisk, Erik}, doi = {10.1109/TIV.2023.3282308}, }
Stability-Informed Initialization of Neural Ordinary Differential Equations

Theodor Westny, Arman Mohammadi , Daniel Jung , and Erik Frisk

In Proceedings of the 41st International Conference on Machine Learning , 2024

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This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how the choice of integration technique implicitly regularizes the learned model, and how the solver’s corresponding stability region affects training and prediction performance. From this analysis, a stability-informed parameter initialization technique is introduced. The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications.
@inproceedings{westny2023stability, title = {Stability-Informed Initialization of Neural Ordinary Differential Equations}, author = {Westny, Theodor and Mohammadi, Arman and Jung, Daniel and Frisk, Erik}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {52903--52914}, year = {2024}, }