TU Delft Repository

The advancements in automated driving hold the potential to improve our daily lives by reducing traffic accidents, minimizing congestion, and allowing more time to spend on other activities. However, realizing these benefits depends heavily on widespread public acceptance, which, according to current research, remains low. Trust and comfort have been indicated as key factors to increase acceptance, highlighting the importance of finding methods of measuring these concepts. This thesis contributes to the development of objective methods for evaluating the emotional state of drivers and passengers in automated vehicles.
In this thesis, an experiment was designed and conducted using a driving simulator to allow participants to experience a ride in an automated vehicle. The aim was to elicit varying levels of comfort by altering the driving style and introducing the presence of a pedestrian, while simultaneously collecting a comprehensive dataset to analyse these comfort levels. This dataset includes continuous subjective comfort ratings given by the participant, vehicle dynamics from the real-world drive on which the simulation was based, webcam footage monitoring the person’s facial expression, Galvanic Skin Response, heart rate (variability), and eye-tracking from 32 participants. Such comprehensive datasets are rare in the literature and provide valuable opportunities for future research to compare different signals and explore their interrelations. From the subjective comfort ratings, it was found that the driving style was a bigger factor than the presence of a pedestrian. Even though the pedestrian did cause a decrease in comfort, the difference between the two driving styles was found to be significantly bigger. For facial expression recognition, a state-of-the-art model was successfully implemented. With minimal lighting conditions, the face could always be detected, and expressions were successfully classified with corresponding emotion labels from the universal set of emotions. Out of the 32 participants, 24 were included in the analyses. Most of these participants (15/24) did not show any detectable reaction in their facial expressions to the critical event. Amongst the 9 participants who did, 8 of them showed a Happy expression, and only 4 showed a Surprise expression. Fear was never dominant. This result shows that, in the current experiment, Facial Expression Recognition is not a reliable method for discomfort detection in Automated Vehicles. Additionally, a neural network was implemented to predict a person’s subjective comfort based on vehicle dynamics and their Galvanic Skin Response (GSR). The model was validated using Leave-One-Out Cross-Validation (LOOCV), where one participant was excluded from the training set and their data was used for testing. The results were promising, as the self-reported comfort and the model-predicted comfort showed a positive correlation across all participants. These findings demonstrate the potential for objective comfort assessment in automated vehicles, reducing the biases inherent in subjective evaluations and paving the way for further research in this field.

This research critically analysed the manufacturing process of a loader bucket with grapple. Such buckets are commonly used to unload dry bulk materials such as wood chips. In order to do this as quickly as possible, these buckets transport tonnes of material each cycle, the buckets themselves have a mass of tonnes too and can be as tall as 4 metres. These buckets are built to order as one-offs or in small batches, optimized for the needs and wishes of the customer. The goal of this research was to reduce the manufacturing lead time of such a bucket from 14 days to ten days or less. The bucket design was changed to accommodate a solution to the problem, but such a change should not increase the module’s weight by more than five percent, nor reduce its functionality in other ways. Each bucket has to remain specific to each customer and their requirements. This research first analysed these requirements and available variations in the bucket design. Following this, the operations in the manufacturing process were identified and analysed with regards to their criticality in the total lead time. Each operation was evaluated regarding its impact on the overall lead time, as well as the dependence on resources available in the factory, such as machinery like a milling machine. Furthermore, the dependency of other operations was also taken into account. According to these factors, the operations with the highest impact on the lead time were identified. From this, various lead time reduction methods were proposed which reduce the impact of these critical operations on the overall manufacturing process. By parallelizing the part with which the manufacturing of the bucket starts, the lead time could be reduced to just more than ten days. To reach the ten day goal, this parallelized part also had to be manufactured more efficiently. For this, six redesigns of the part were devised which do not require machining. These were evaluated in a survey among six employees from the engineering, planning and manufacturing departments. The most promising designs could reduce the expected lead time of the part itself by two days. By combining the suggested parallelization, along with the shortened manufacturing of the mounting plate without machining, the goal of a lead time of ten days could be achieved.

Renal cell carcinoma (RCC), the predominant form of kidney cancer, has increased in incidence and mortality. Partial nephrectomy (PN) is the preferred treatment for resectable renal lesions, with robot-assisted partial nephrectomy (RAPN) combining minimally invasive surgery and robotic precision. However, RAPN challenges surgeons in accurately identifying resection borders within the renal parenchyma. This thesis evaluates the feasibility of integrating electromagnetic tracking (EMT) navigation into RAPN to improve resection guidance. Navigation overlays patient-specific preoperative imaging onto the surgical field, using real-time tracking of surgical instruments and a kidney-affixed sensor to account for kidney movements. The primary objective was to develop and validate an EMT navigation setup for RAPN, evaluating its accuracy and impact on the workflow.
We developed a navigation setup using an electromagnetic tracking system (EMTS) of NDI (Northern Digital Inc., Waterloo, Ontario, Canada), with custom software interfaces and tracking instruments. Conventional RAPN resection volumes were quantified to assess current over-resection and set a comparative standard. EMTS integration in RAPN was evaluated by measuring tracking accuracy in the surgical setup. Accuracy of point- and surface-based registrations were assessed in both phantom and patient settings, including a clinical workflow evaluation using a System Usability Score (SUS).
Our analysis of conventional RAPN demonstrated over-resection in three of four patients, with 62%, 64%, and 165% more healthy parenchymal tissue removed than the 4 mm safety margin required. EMTS evaluation in RAPN showed a median position error of 0.5 mm (0.3 – 0.7 mm) and a median orientation error of 0.3° (0.2 – 0.3°) within the clinically relevant kidney area, indicating minimal distortion from the Da Vinci Surgical System and surgical table. In phantom studies, registration techniques demonstrated a root-mean square error (RMSE) of 1.8±0.1 mm and a target registration error (TRE) of 7.1±1.9 mm. In patient setting, the TRE varied among the three included patients: 2.6±2.5 mm, 44.4 mm, and 3.0±2.5 mm. While EMT navigation showed accuracy potential for enhancing RAPN resection outcomes (< 4.0 mm), an average SUS score of 64.2±2.9 indicated room for improvement. Key focuses included ensuring kidney sensor stability and reducing the time required for navigation-related procedures.
The developed EMT navigation setup provides real-time image-guidance in RAPN, achieving the highest intraoperative precision with point and US-based surface registrations and the kidney sensor placed near the target. Future clinical evaluations are needed to validate this finding and improve its usability for lesion resection. Ultimately, integrating navigation could increase RAPN use over radical nephrectomy, improving RCC treatment outcomes.

The 'hammerhead fairing' (HHF) launcher configuration uses a payload fairing that has a larger diameter than the launch vehicle. This allows for larger payloads to be carried using the same launcher components. However, the HHF leads to high aerodynamic forcing in the transonic regime called buffeting. If unaccounted for, buffeting can lead to launcher failure. The flow phenomena associated with buffeting were investigated on the Vega-E launcher, which is currently being developed by the ESA, using Pressure Sensitive Paint (PSP) in the TST-27 tunnel.

PSPs are a relatively new flow measurement technique that allows for quantitative measurements of surface pressure levels in a non-intrusive manner. High spatial resolution, which is equivalent to using millions of pressure taps, can be achieved using PSP. This allows for CFD-like surface pressure resolution to be obtained from wind tunnel experiments. However, PSPs had previously never been successfully implemented in Delft. Therefore, a test set up was devised for experiments in the TST-27. The final implementation was found to have a pressure uncertainty of at most 2.5 kPa, with a spatial resolution of 0.088mm.

First, validation measurements were done on the Coe and Nute Model 11. The Model 11 is a common HHF test case in literature that is used to study the buffeting phenomenon. Results obtained in the TST-27 were compared to historical data and showed good matching when accounting for blockage. After the validation, measurements were taken on the Vega-E launcher at different Mach numbers and angles of attack to quantify the effects of each variable on the pressure field. Lastly, the impact of protuberances on the model was studied. The protuberances that were used mimic those that will be present in the full-scale launcher.

Pore structure characteristics of cementitious materials play a critical role in the transport properties of concrete structures. This paper develops a novel framework for modeling chloride penetration in concrete, considering the pore structure-dependent model parameters. In the framework, a multi-scale transport model was derived by linking the chloride diffusivities with pore size distributions (PSDs). Based on the three-dimensional (3D) microstructure generated by “porous growth” and “hard core-soft shell” methods, two sub-models were computationally developed for determining the multi-modal PSDs and pore size-related chloride diffusivities. The predicted results by these series of models were compared with corresponding experimental data. The results indicated that by adopting pore size-related diffusivities, even if the total porosities were the same, the proposed multi-scale chloride transport model could better capture the effect of different PSDs on chloride penetration profiles, while the model without pore structure-depended parameters would ignore the differences. Compared with the reference transport models, which adopt averaged chloride diffusivities, the chloride penetration depths predicted by the proposed multi-scale model are in better agreement with experimental data, with 10%–25% reduced prediction error. This multi-scale transport model is hoped to provide a novel computational approach on 3D microstructure generation and better reveal the underlying mechanism of the chloride penetration process in concrete from a microscopic perspective.