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Senast publicerade

Road Section Weather and Vehicle Energy Consumption Modeling
(2026) Eriksson, Otto; Liu, Kaiyuan
Accurate vehicle energy consumption modeling for heavy-duty trucks requires precise characterization of aerodynamic drag and rolling resistance, both of which are highly sensitive to local weather and road conditions. Existing models often rely on standard atmospheric assumptions or dedicated on-board sensors, which limits their ability to capture real-world operating conditions. To address this, the present work proposes an integrated physics-based modeling framework that links road-segment level meteorological data to vehicle motion resistance without requiring additional sensing hardware. For the wind prediction model, a topographical model is used as a base and further corrected with a two-layer wind speed correction model developed to translate regional meteorological station measurements into local road-level wind estimates. The successive layers account for a global power law bias and geographic environment effects through clustering of road segments into sheltered and wind-amplified categories. Validated against over 200 repeated real-world journeys on RV (Riksväg) 40, Sweden, the model achieves a 48.4% reduction in mean absolute error over the regional baseline, consistently across seasons and travel directions, compared to using measurements from regional meteorological stations. A wind direction model in the form of a polynomial is incorporated to complete the wind characterization. For the rolling resistance component, a two-state tire thermodynamic observer is formulated, estimating tire bulk temperature from standard Tire Pressure Monitoring System (TPMS) measurements and an external temperature sensor via a Kalman filter. This enables model-based prediction of temperature-dependent rolling resistance as a function of ambient conditions and driving dynamics, without dedicated thermal sensors. The proposed framework provides a scalable, low-cost approach to weather-aware energy consumption forecasting, directly applicable to range prediction and route planning for heavy-duty trucks operating under dynamic real-world road section conditions.
Cam Phaser Simulation in GT-Suite
(2026) Sankar, Sarath
Cam phasers enable variable valve timing by changing the angular position of the camshaft relative to the crankshaft. In heavy-duty diesel engines, cam phasing can support functions such as thermal management, engine braking, and potential replacement of certain conventional engine components. Understanding the internal behaviour of a cam phaser is therefore important for further system development. However, existing supplier models provide limited access to the internal hydraulic and mechanical behaviour of the system. This thesis develops an in-house system-level model of a hydraulic Cam Torque Actuated vane phaser in GT-SUITE. The model combines the hydraulic, mechanical, and electrical domains of the system, with particular focus on the Oil Control Valve and the internal oil flow paths governing phaser operation. The model is calibrated against test-cell measurements. The results show that the model captures the actuator and cam phasing behaviour observed in the measure ments, although some deviation remain. Beyond reproducing measurable system responses, the physics-based model enables investigation of internal quantities and oil flow behaviour that cannot be directly measured in the test setup. The de veloped model therefore provides a basis for improved understanding and further development of the cam phaser system.
Enhancing Web Accessibility Remediation with LLM-based IDE Extensions
Luo, Dantong
AI coding assistants offer opportunities for web accessibility support, but evidence on how accessibility-focused guidance affects code accessibility and developer experience remains limited. This thesis presents A11yHelper, a Visual Studio Code extension integrated with GitHub Copilot Chat, and compares its accessibility quality, perceived usability, and tool preference with GitHub Copilot and CodeA11y. The evaluation combined a fixed-prompt experiment and a controlled user study with 18 participants. Both covered five tasks from two repositories. The analysis used task-specific three-level quality ratings, the SUS score, preferences between the two tools used, and descriptive categorization of written reasons. Quality differences depended on the task. A11yHelper had higher mean scores than GitHub Copilot on form labeling and keyboard navigation in both evaluations, while GitHub Copilot matched or exceeded its means on the remaining tasks. Both scored higher than CodeA11y on the task requiring information distributed across a larger file. Alternative text remained difficult, with only two of 36 user-study artifacts rated Good. Mean SUS scores were 79.38 for GitHub Copilot, 76.46 for A11yHelper, and 59.58 for CodeA11y, preference counts followed the same ordering. The study contributes an integrated accessibility assistant and descriptive evidence of its task-specific strengths and limitations.
Reactive Whole-Body Control for Grasping Moving Objects with Mobile Manipulators
(2026) Li, Dongxiang; Zhou, Shijie
Grasping moving objects remains challenging for fixed-base manipulators because their limited workspace prevents them from continuously tracking objects that move beyond their reachable area. This limitation is particularly important in human–robot collaborative scenarios. In this work, we propose a framework for grasping moving objects with a mobile manipulator that dynamically repositions its base to expand its reachable workspace and improve grasp feasibility. To coordinate the system motion, we adopt a reactive whole-body control strategy that treats the mobile base and manipulator as a unified kinematic system. As the object moves, the target pre-grasp pose is updated based on the estimated object pose, allowing the robot to track and approach a grasp-relevant target. The proposed method is validated in both simulation and real-world experiments, demonstrating the effectiveness of the unified control strategy for coordinated tracking and grasp-oriented motion generation when interacting with moving objects.
Retail AI Roadmapping: A Data-Driven Portfolio Decision Engine
(2026) Granskvist, Ted
Retail companies face a growing number of possible AI initiatives, but the harder problem is often not idea generation. It is deciding which use cases should be prioritised, which ones fit the company context, and which ones are realistic to move beyond experimentation. This thesis addresses that problem by developing a structured decision-support framework for AI use-case prioritisation in retail, based on an organisation × use-case representation that combines company-side attributes, use-case-side attributes, and computed fit logic. The empirical material comes from interviews with 33 Nordic retail companies across Sweden, Norway, Denmark, and Finland. The sample was weighted towards larger retailers, with 16 companies in the SEK 1–10bn annual revenue range and 12 above SEK 10bn, and included senior respondents such as CIOs, CTOs, CDOs, and heads of data, technology, or AI. Across these interviews, respondents listed 151 AI use cases, of which 55 were coded in greater depth with structured attributes and managerrated impact assessment. The study combines descriptive portfolio analysis, baseline impact modelling, model interpretability analysis, cluster-strength analysis, and qualitative interpretation. The baseline impact models showed some predictive signal in the structured interview attributes, especially for focused non-fit feature sets with tree-based ensemble models. However, explanatory performance remained modest, and predictions were compressed towards the middle of the impact scale. The models should therefore be read as support for prioritisation rather than as precise impact predictors. The results suggest that AI use-case prioritisation in retail should not be reduced either to GenAI adoption or to one universal ranking of use cases. GenAI adoption was widespread, but traditional ML use cases often showed clearer operational impact in the observed material. Data availability was generally stronger than data quality and accessibility, while process-change burden appeared more prominent than technical integration burden. In the observed-only cluster-strength results, internal delivery, high data readiness, high change capacity, and forecasting or inventoryplanning use cases appeared as recurring strong patterns, although these should be interpreted as signals rather than general rules. Overall, the thesis supports a context-sensitive playbook logic. Retailers should prioritise AI use cases by asking where the operational pain is, whether the relevant data is reliable and accessible, what portfolio role the use case should have, what delivery route fits the context, and what operating model is needed after launch.