Informatik/Technik
Dauerhafte URI für die Sektionhttps://epub.uni-luebeck.de/handle/zhb_hl/4
Listen
Auflistung Informatik/Technik nach Instituten/Kliniken "Institut für Medizinische Elektrotechnik"
Gerade angezeigt 1 - 7 von 7
- Treffer pro Seite
- Sortieroptionen
Item Combining magnetic particle imaging and magnetic fluid hyperthermia(2024) Wei, HuiminItem Interaction-aware model predictive control for autonomous highway driving(2026) Zhang, XiaorongAutomated driving technology has developed rapidly over the past decades, profoundly transforming traditional modes of transportation. It offers significant conveniences by replacing human drivers in simple scenarios and improves safety through driver assistance systems. However, despite these advances, achieving full automation still faces substantial challenges. One critical challenge is accurately predicting the motion of surrounding vehicles in real time and incorporating these predictions into the control of autonomous vehicles to ensure safety. The diversity in individual driving behaviors, coupled with the complexity of modeling interactions among traffic participants under uncertainties, makes this problem particularly difficult. To provide insights into these challenges, this thesis focuses on interaction-aware traffic prediction and the safe control of autonomous vehicles on highways. Highways are chosen as the study environment due to their structured lanes and lower traffic density. The aim is to establish a research foundation in this setting that can later be extended to more complex traffic environments, such as urban roads. Specifically, this thesis investigates two primary research directions to address these challenges. The first direction separates the tasks of interaction-aware traffic prediction and safe control of autonomous vehicles. The second direction aims to integrate these tasks into a unified control architecture for greater simplicity and efficiency. In the first research direction, diverse intention-based models along with the Interacting Multiple Model Kalman Filter (IMM-KF) algorithm are employed to estimate and predict vehicle motion states while considering interactions. Possible vehicle motion maneuvers are represented by a finite number of normal scenarios. Additionally, a “worst-case” scenario is considered along with normal scenarios in a Scenario-based Model Predictive Control (SCMPC) architecture to ensure the safety of the autonomous vehicle. Moreover, to reduce the conservatism of the control strategy associated with considering the “worst-case” scenario, a new Contingency Model Predictive Control (CMPC) scheme is explored with time-varying prediction horizons and an enlarged terminal set. In the second research direction, the Minimizing Overall Braking Induced by Lane Change (MOBIL) model is employed to find out the possible lanes that vehicles may occupy based on vehicle interactions. With this lateral traffic prediction, the longitudinal states of surrounding vehicles are modeled simultaneously with those of the autonomous vehicle within a Model Predictive Control (MPC) structure. They are determined by minimizing the collective control costs of all vehicles through dynamic interaction-aware mechanisms. All proposed control architectures are validated in a high-fidelity IPG CarMaker simulation environment. Simulation results demonstrate the effectiveness of these approaches in safely controlling autonomous vehicles while considering vehicles’ interactions on highways. The findings of this thesis provide new perspectives on addressing challenges in high way autonomous driving and establish the foundation for extending safe control strate gies to more complex traffic scenarios involving interactive behaviors. This research advances the understanding of interaction modeling in multi-agent traffic environments, contributing structured approaches to represent and leverage vehicle interactions for safer autonomous control. The modular design of the proposed frameworks facilitates their integration with broader automated driving systems, such as perception and planning modules. Moreover, validation in a high-fidelity simulation environment offers a practical reference for a potential real-world implementation.Item Lightweight, transparent, and uncertainty-aware deep learning for diabetic retinopathy grading(2025) Siebert, Marlin SebastianItem Item MPC-based vehicle trajectory tracking using machine learning for parameter optimization and fault detection(2025) Lubiniecki, ToniThis thesis explores advancements in trajectory tracking control and fault detection within automated vehicle systems, focusing on two main areas: developing a learning-based model predictive control algorithm to enhance tracking accuracy and evaluating various neural networks as fault detection systems for trajectory tracking controllers. Both parts are assessed in a high-fidelity simulation environment. The first part presents two adaptive model predictive controllers that use vehicle information, trajectory data, and tracking information to adapt the vehicle model within the model predictive control system, compensating for lost tracking accuracy due to model mismatches. One approach employs a trajectorydynamic lookup table, while the more advanced approach uses Gaussian process regression with clustering. A thorough simulation study on real-world racetracks with varying dynamics demonstrates that the advanced approach effectively manages condition changes, significantly improves tracking performance, handles unknown trajectories with similar improvements, and memorizes adapted behavior through clustering. The second part evaluates the effectiveness of four types of neural networks as fault detection systems. These networks detect changes in the vehicle, environmental shifts, or discrepancies between the applied vehicle model and the real vehicle. Trained a priori through supervised learning, the networks use tracking information, controller outputs, and vehicle data. The evaluation distinguishes between known and unknown fault conditions. The results suggest that neural networks are generally suitable for fault detection systems. Differences in effectiveness among the network types are minor for known fault conditions but more significant for unknown conditions. Integrating adaptive model predictive control and neural network-based fault detection systems shows promise for developing robust and fault-tolerant control systems, enhancing accuracy and maintaining operational integrity in dynamic environments for trajectory tracking.Item Non-invasive estimation of respiratory effort(2025) Graßhoff, JanItem Probabilistic model-based anomaly detection for the European X-ray free electron laser(2023) Nawaz, Ayla Schamineh