| 1. |
- Mohammadi, Arman, et al.
(författare)
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Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems
- 2023
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Ingår i: IFAC PAPERSONLINE. - : ELSEVIER. ; , s. 2909-2914
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Konferensbidrag (refereegranskat)abstract
- Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems. One application is the residual evaluation of technical systems where model predictions are compared with measurement data to create residuals for fault diagnosis applications. While recurrent neural network models have been shown capable of modeling complex non-linear dynamic systems, they are limited to fixed steps discrete-time simulation. Modeling using neural ordinary differential equations, however, make it possible to evaluate the state variables at specific times, compute gradients when training the model and use standard numerical solvers to explicitly model the underlying dynamic of the time-series data. Here, the effect of solver selection on the performance of neural ordinary differential equation residuals during training and evaluation is investigated. The paper includes a case study of a heavy-duty truck's after-treatment system to highlight the potential of these techniques for improving fault diagnosis performance.
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| 2. |
- Westny, Theodor, 1993-
(författare)
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Data-Driven Interaction-Aware Behavior Prediction for Autonomous Vehicles
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Future progress toward the realization of fully self-driving vehicles still re-quires human-level social compliance, arguably dependent on the ability to accurately forecast the behavior of surrounding road users. Due to the inter-connected nature of traffic participants, in which the actions of one agent can significantly influence the decisions of others, the development of behavior pre-diction 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 pre-diction 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.
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| 3. |
- Westny, Theodor, et al.
(författare)
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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
- 2023
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Ingår i: 2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV. - : IEEE. - 9798350346916 - 9798350346923
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Konferensbidrag (refereegranskat)abstract
- 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 Heuns can greatly improve predictions.
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| 4. |
- Westny, Theodor, 1993-, et al.
(författare)
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MTP-GO : Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 8:9, s. 4223-4236
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Tidskriftsartikel (refereegranskat)abstract
- 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. Multimodal probabilistic predictions are obtained 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.
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| 5. |
- Westny, Theodor, et al.
(författare)
-
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:9, s. 4223-4236
-
Tidskriftsartikel (refereegranskat)abstract
- 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. Multimodal probabilistic predictions are obtained 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.
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