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Road Friction Aware Adaptive Cruise Control using Robust Nonlinear Model Predictive Control with Uncertainty Quantification

(2025) Crnic, Mubina; Koutsoftas, Sotiris

Road friction is a fundamental factor affecting vehicle safety, especially under adverse weather conditions such as rain, snow or ice. Reduced road grip increases stopping distances and the likelihood of loss of control, contributing to a significant number of traffic accidents each year. While modern Advanced Driver Assistance Systems (ADAS) are designed to enhance driving safety, many systems still lack the ability to adapt their behaviour in real time to varying road friction levels, limiting their effectiveness in low-friction or rapidly changing weather conditions. This thesis addresses this limitation by developing a friction-aware, curvature-adaptive Adaptive Cruise Control (ACC) framework based on a Robust Nonlinear Model Predictive Control (NMPC) formulation. The controller anticipates road curvature and spatially-varying friction by incorporating predicted profiles into the optimization problem. To account for uncertainty, friction is represented through position-dependent bounds in the MPC, while stochastic realizations of friction are generated via Beta sampling and the lead vehicle’s acceleration varies randomly within feasible physical limits. These variations are reflected in dynamic safety constraints that adapt to evolving conditions ahead of the ego vehicle. Soft constraint relaxation is introduced to maintain feasibility under conflicting demands such as sudden friction drops or aggressive lead vehicle deceleration. Despite the nonlinear nature of the problem and the presence of uncertainty, the controller operates using an efficient CasADi-based implementation in MATLAB. Simulation results demonstrate that the proposed framework achieves robust, adaptive cruise control under friction and road curvature changes with stochastic disturbances. The integration of environmental awareness into predictive control enables safer, more responsive vehicle behaviour without sacrificing passenger comfort, and highlights the feasibility of embedding the friction information into future ADAS systems.

Aerodynamisk Optimering för Förbättrad Effektivitet i Airborne Wind Energy-system

(2025) Dubad, Abdirahman; Lakpour, Atrin; Ayman, Mohammad Ali; Amiri, Mohammad; Jaffari, Mustafa; Van Leest, Nils

Detta projekt undersöker olika strategier för att förbättra den aerodynamiska effektiviteten hos linan som är kopplad till draken, med särskilt fokus på att minska luftmotståndskraften. Genom numeriska simuleringar, experiment i vindtunnel och modellering konstaterades en viktig upptäckt: cirka 50 % av det ackumulerade luftmotståndet kommer från de sista 40 meterna av linan, vilket gör denna del mycket lämplig för modulär optimering. I ett annat resultat uppnåddes en uppskattad minskning av det totala luftmotståndet med 51.5 % genom att använda en kombination av en cylindrisk lina täckt med ett aerodynamiskt skal format som en vinge.

Canonicity of the Mahlo Universe

(2025) Kubánek, Ondrej

The thesis presents a proof of canonicity of the external Mahlo universe using the gluing technique. The used metatheory is an extensional type theory extended with indexed induction-recursion to express the reducibility predicate for the external Mahlo universe. This is motivated by the fact that the Mahlo universe can express certain inductive-recursive definitions and so understanding the metatheory for the external Mahlo universe should help with metatheory for induction-recursion.

Natural Language Processing and Large Language Models for Automation of Compliance Tracing

(2025) Forsell, Maximilian; Erlandsson Hollgren, Eric

Compliance is a costly and time-consuming task that most, if not all, firms must perform. As such, automating parts of the compliance process could be highly valuable. This thesis aims to investigate challenges faced by European software-intensive firms in their compliance processes, identify automation opportunities, and develop a Natural Language Processing- and Large Language Model-based software artifact to automate compliance tracing between company guidelines and normative requirements. The thesis followed the Design Science Research approach, and as such, the research was conducted in close collaboration with industry practitioners. The challenges and automation opportunities were identified together with seven interviewees from four different companies, and the final software artifact, dubbed TraceAlign, was developed and evaluated in focus groups with a total of twelve unique participants from two companies. The identified challenges ranged from organizational- and management-related to specifics inherent to the specifications of normative requirements. Automation opportunities related mainly to the management of requirements, company guidelines, and compliance evidence, of which this thesis focuses specifically on the task of compliance tracing of company guidelines to normative requirements. The final software artifact, TraceAlign, was considered to be time- and cost-saving by the focus group participants, but could perhaps be made more accurate. We conclude that there are many challenges with compliance that could potentially be automated using Natural Language Processing and Large Language Models.

Designing to Bridge the Gender Gap in Micromobility

(2025) Can Zois, Alexander; Xerxes Falk, Bill

Despite the popularity of micromobility services, there is a prominent gender gap in users of micromobility, where men who ride e-scooters and e-bikes greatly outnumber women. This master thesis project investigates the barriers to women’s micromobility adoption that contribute to this gap, and subsequently proposes a design solution addressing these barriers. Adopting a user-centered approach, qualitative interviews were conducted with both user and non-user women to identify and understand their safety and accessibility needs. The primary barrier to women’s adoption of micromobility solutions was found to be an interconnected network of personal safety concerns, gender stereotypes, varying travel needs, and negative preconceived notions surrounding micromobility. Taking into account these findings, solutions were ideated upon and evaluated. A high-fidelity prototype was created to exemplify how the discovered barriers may be addressed through design solutions. This resulting prototype was dubbed Training Mode, a free browser-based e-scooter training platform that addresses the participants’ needs for more transparency and instruction on how to navigate the functions of an e-scooter. Initial evaluations suggest that Training Mode succeeds in enticing hesitant non-users to engage with e-scooters, prompting future work to investigate its potential to impact on female ridership.