Examensarbeten för masterexamen
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- PostHolistic Diagnosis via Multimodal Foundation Models(2024) Pauli, Oskar; Chalmers tekniska högskola / Institutionen för elektroteknik; Graell i Amat, Alexandre; Ceccobello, Chiara; Östman, JohanThe healthcare domain has data in many different forms, or modalities. They can be in the form of x-ray images, time-series of certain events like heart rate or blood pressure, textual data from notes etc. Medical practitioners uses many different modalities every day to make informed and sound decisions. With the recent success of small and large language models, it is natural to try and incorporate them with multimodal capabilities in the healtcare domain. This thesis seeks to investigate how well small language models can perform on predictive tasks in healthcare using multimodal data. To explore this, projectors that project data from different sources to the embedding space of a language model was developed. While the results show that a multimodal language model is better than a single-sourced version, it is still being outperformed by the XGBoost model. Even though it is being outperformed, the model proposed shows promise in regards to generalizability, potentially streamlining predictive tasks in healthcare. The thesis argue that even if improvements needs to be made and the challenges it poses can be difficult to handle, further advancements can lead to facilitating medical practitioners in a very efficient way.
- PostSensing and Communicataion using FMCW Radar(2024) Janetzko, Leon; Rex, Alexander; Chalmers tekniska högskola / Institutionen för elektroteknik; Wymeersch, Henk
- PostPerception and Decision-making with YOLO and ChatGPT Integration for Intelligent Robot Navigation(2024) Krishnamurthi, Suraj; Hjertvist, Oscar; Chalmers tekniska högskola / Institutionen för elektroteknik; Ramirez-Amaro, Karinne
- PostStochastic MPC for Autonomous Vehicles in Uncertain Situations(2024) Zhang, Qun; Salih, Saeed Ponnarikkal; Chalmers tekniska högskola / Institutionen för elektroteknik; Murgovski, Nikolce; Börve, Erik; Laine, LeoAbstract In this thesis, an Model Predictive Control (MPC) based trajectory planning algorithm is first introduced for controlling trucks on highways. Given the uncertainties that exist between theoretical models and real vehicles, this study further analyzes these uncertainties and proposes an Stochastic Model Predictive Control (SMPC) based trajectory planning algorithm. The algorithm avoids collisions by tightening constraints and is validated in the CARLA simulation environment. Experimental results show that the SMPC-based trajectory planning algorithm has obvious advantages in terms of safety performance compared with the standard MPC. However, the method also sacrifices certain driving performance and increases computational complexity, which is mainly due to the tightened traffic constraints. This study not only verifies the effectiveness of SMPC in handling uncertainty and enhancing safety but also provides both an experimental and theoretical basis for future work.
- PostThe Multimodal IMR-TrajNet - End-to-end Deep Ego Trajectory Prediction using Front-facing Camera Images and Standard Definition Maps(2024) Bhaholpolbhayuhasena, Napat; Laveno Ling, Arvid; Chalmers tekniska högskola / Institutionen för elektroteknik; Hammarstrand, Lars; Vinkås, JakobAbstract Ego trajectory prediction is crucial for the development of autonomous vehicles (AV), enabling them to navigate complex environments safely and efficiently. While Highdefinition (HD) maps are commonly used due to their detailed information, they come with high computational costs and scalability issues. Standard Definition (SD) maps, on the other hand, offer a more scalable and cost-effective solution, providing sufficient conceptual context to enhance prediction models. Moreover, these maps may also contain navigation routes that further indicate the future behavior of the AV. By integrating SD maps with front-facing camera images, we can capture the real-world scenario more accurately, addressing potential inaccuracies in the maps and improving the robustness of the prediction. Our method explores different ways to extract and utilize features of SD maps for trajectory prediction, employing both Graph Neural Network (GNN)-based and transformer-based map methods for conducting this extraction. We tested various approaches for fusing the features from these modalities, including normal concatenation and cross-attention mechanisms, and for decoding the trajectories. Additionally, we introduced an auxiliary task to guide the model in learning more accurate trajectory shapes and utilized multimodal predictions to capture the inherent variability in possible driving paths. The results demonstrate a significant improvement in trajectory prediction accuracy, with our approach achieving up to four times better performance compared to the baseline model that relies solely on visual data. The inclusion of SD maps and route information not only reduces errors but also enhances the model’s robustness across various driving scenarios. These findings highlight the potential of using SD maps to advance autonomous vehicle technology.