Theodor Westnys licentiate thesis presentation

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Respondent: Theodor Westny
Opponent: Senior associate professor Paolo Falcone, Chalmers/University of Modena e Reggio Emilia, Italy
Supervisor: Professor Erik Frisk, Linköping University
Date: 2023-05-12
Time: 10:15
Place: Ada Lovelace, Campus Valla, Linköping

Link to thesis:

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-193083

Links to included papers:
Paper 1: https://doi.org/10.1109/ITSC48978.2021.9564948 “Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques”
Paper 2: https://arxiv.org/abs/2302.00735 “MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs”
Paper 3: https://arxiv.org/abs/2304.05116 “Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction”

Abstract:

Future progress toward the realization of fully self-driving vehicles still requires human-level social compliance, arguably dependent on the ability to accurately forecast the behavior of surrounding road users.

Due to the interconnected nature of traffic participants, in which the actions of one agent can significantly influence the decisions of others, the development of behavior prediction methods is crucial for achieving resilient autonomous motion planning. As high-quality data sets become more widely available and many vehicles already possess significant computing power, the possibility of adopting a data-driven approach for motion prediction is increasing.

The first contribution is the design of an intention-prediction model based on autoencoders for highway scenarios.Specifically, the method targets the problem of data imbalance in highway traffic data using ensemble methods and data-sampling techniques. The study shows that commonly disregarded information holds potential use for improved prediction performance and the importance of dealing with the data imbalance problem.

The second contribution is the development of a probabilistic motion prediction framework. The framework is used to evaluate various graph neural network architectures for multi-agent prediction across various traffic scenarios. The graph neural network computes the inputs to an underlying motion model, parameterized using neural ordinary differential equations. The method additionally introduces a novel uncertainty propagation approach by combining Gaussian mixture modeling and extended Kalman filtering techniques.

The third contribution is attributed to the investigation of combing data-driven models with motion modeling and methods for numerical integration. The study illustrates that improved prediction performance can be achieved by the inclusion of differential constraints in the model, but that the choice of motion model as well as numerical solver can have a large impact on the prediction performance. It is also shown that the added differential constraints improve extrapolation properties compared to complete black-box approaches.

The thesis illustrates the potential of data-driven methods and their usability for the behavior prediction problem. Still, there are additional challenges and interesting questions to investigate—the main one being the investigation of their use in autonomous navigation frameworks.