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Development of a Driver-in-the-Loop Advanced Driver Assistance Systems Prototyping Platform

(2025) Chouhan, Abhay; Fäldt, Linus; Jonsson, Elliot; Liao, Fanxiang; Sridharraju, Prakash Raju; Wang, Jingyu

This project presents the development of a small scale Driver in the Loop Advanced Driver Assistance System prototyping platform based on a MentorPi robot vehicle and a modular Robot Operating System 2 software architecture. The platform integrates human driver inputs (steering wheel, pedals, and gear selection), onboard sensing (monocular camera and 360° LiDAR), and real time visualisation into a closed loop test setup for safe and repeatable indoor testing. Functionality is distributed across Robot Operating System 2 nodes for driver input handling, motion control, LiDAR processing, state aggregation, and live camera streaming with information overlays. A simplified Automatic Emergency Braking function is implemented using an Enhanced Time to Collision, using a stopping distance trigger. Rather than relying on a fixed distance threshold, this approach estimates whether the vehicle can safely stop before reaching an obstacle by accounting for the speed and braking capability, making the intervention more representative of practical safety behaviour. Two operating modes were validated: a baseline browser based camera view and a virtual reality mode using a Meta Quest 3. In the VR configuration, the camera feed is accessed via an HTTP snapshot endpoint, while head tracking data are transmitted over User Datagram Protocol to control the pan–tilt camera. Results show stable baseline teleoperation, with 20 consecutive laps completed without system restart, and successful execution of a combined VR driving–slalom, Automatic Emergency Braking scenario in four out of five runs. However, VR operation occasionally led to a paralyzed control state, indicating integration and stability limitations under increased system load. Beyond functional validation, the platform is intended to enable rapid prototyping and early stage evaluation of Active Safety and Advanced Driver Assistance Systems concepts. Its low cost, modular design, and safe indoor operation make it particularly suitable for pedagogical activities at Chalmers University of Technology, supporting hands on learning and experimentation in courses related to Active Safety and Driver in the Loop system development.

Intelligent Cabins – Energy Efficiency and Passenger Comfort in BEVs

(2025) El Masri, Mohammed Ali; Dineshwar, Rishi; Chowdhury, Saeed Al Rehman; Sapre, Saket Sharad; Nandagiri, Upasana; Chandrasekar, Vinoth Kanna

This technical report investigates strategies for enhancing energy efficiency and occupant thermal comfort in Battery Electric Vehicle (BEV) cabins during cold climate operations. Using high-fidelity Computational Fluid Dynamics (CFD) in STAR-CCM+, the study characterizes the complex relationship between HVAC parameters, specifically inlet temperature and mass flow rates, and human physiological responses. The research integrates advanced thermo-physiological models, including the Fiala and Berkeley models, to provide a detailed analysis of local thermal sensation and comfort across diverse occupant demographics. Key findings from the parametric study indicate that a vane inlet temperature of 32 °C, resulting in an average cabin temperature of approximately 24.6 °C, provides the highest thermal comfort for both male and female occupants, achieving a Predicted Percentage of Dissatisfied (PPD) of nearly 5 %. The study demonstrates that while adjustments to the ventilation mass flow rate have a negligible impact on occupant comfort, reducing the flow from 0.2 kg/s to 0.1 kg/s can yield measurable energy savings, potentially extending vehicle range by 3–5 km during winter driving. Additionally, the results highlight the significant influence of solar loads on thermal perception, noting that occupants feel “chilly” at 24 °C when radiative heating is absent. This work serves as a foundation for designing intelligent, occupancy-aware climate control systems that balance passenger well-being with vehicle performance.

Performance comparison of simulation models and parameters for engine CFD applications.

(2026) Oinonen, Ilmari

In this thesis, the performance of computational fluid dynamics (CFD) simulation models and parameters are systematically compared for an engine model based on the Imperial college experimental engine. A set of simulation models and parameters were chosen to be studied. For each model and parameter, a few different options were chosen and simulation cases created for each possible different combination of these options. A custom code was written for creating the cases out of all the combinations of model and parameter options. The simulations for this thesis were performed with OpenFOAM, which is an opensource CFD software. The engine model has been created at Wärtsilä and used in-house meshing tools to create a moving mesh. After the simulations had ran, the results were post-processed using a custom code written for this thesis. The post-processed results were directly compared to results from a high-fidelity direct numerical simulation (DNS) study based on the Imperial college case, which has great agreement with the experimental results. For each simulation, two metrics were calculated that measured the difference between simulation and DNS results. These metrics were used to assess the performance differences between cases with different model and parameter combinations, and thus the performance of the models and parameters themselves. The models and parameters studied in this thesis were the turbulence model, velocity advection scheme, maximum Courant number, mesh resolution and the number and thickness of surface inflation layers of the mesh. These were divided into three individual studies. A few of the turbulence models had clearly better performance than the others. Change in the velocity advection scheme, maximum Courant number and surface inflation parameters had only slight effects on performance. An increased mesh resolution generally lead to better performance.

Dynamic load of timber truck

(2025) Munavalli, Ramkumar Huleppa; Larsson Rosén, Viktor; Vasudevan, Rohan Kumaar; Wu, Boxuan

This project investigates the dynamic load generated by heavy timber truck combination as part of a collaborative research effort between Chalmers university of Technology, NTNU and volvo trucks. The overarching goal of the research is to improve the understanding of how trucks and their loads influence bridge structure, with the long term objective of enabling safe reclassification of exiting swedish bridge fie higher load limits (BK4). In this project,the focus on developing a simplified yet representative dynamics model of a timber truck combination that can later be used for studying Dynamic Amplification factor(DFA) and truck-bridge interaction. The part involved deriving the equation of motion for the truck and trailer, identifying and estimating key model parameters, and implementing the model in a suitable simulation environment. Parametric studies were carried out to analyze how factors such as road surface irregularities, vehicle speed, suspension characteristic and axle configuration affect the resulting dynamic loads. Furthermore, the project included the planning and preparation of experimental test using an instrumented timber truck for future model validation. The developed model and proposed testing methodology together provide a foundation for accurately simulating the dynamic behaviour of heavy vehicle and for supporting future studies on the interaction between vehicle and bridges.

Self-Supervised Fixed-Scene Adaptation for Object-Detection in Real-Time Surveillance: A Comparative Study of YOLO11 and RF-DETR

(2026) Justad, Jacob

This thesis investigates self-supervised fixed-scene adaptation for real-time objectdetectors in an edge-computing surveillance context. While modern object-detectors achieve strong results on general-purpose benchmarks, deployment in static camera scenes introduces distinct challenges: domain shift to a specific viewpoint, limited availability of scene-specific labels, and stringent compute and memory budgets ondevice. At the same time, the stationary background of surveillance footage provides exploitable structures, as do their temporal dependencies of video-frames. This study conducts a comparative analysis of two state-of-the-art object detection architectures: the Transformer-dominant RF-DETR and the convolutional neural network (CNN)- dominant YOLO11. The thesis employs the 100Scenes dataset to represent a broad range of surveillance environments. Experimental results demonstrate that RF-DETR consistently achieves higher accuracy, smoother convergence, and greater robustness than YOLO11, albeit with higher hardware demands. In contrast, YOLO11 variants (with a frozen backbone) leverage the larger trainable capacity of the neck and head to enable high scene-specific adaptability. While this yields significant gains under quality labeling, it tends to increase sensitivity to imperfect pseudo-labels and the risk of overfitting. Furthermore, by systematically varying model scales, adaptation strategies and environmental conditions the experimental design yields more than 3400 distinct runs. First the work examines the extent to which smaller, specialized models can match the approach of substantially larger models. The experimental results show that a small specialised model can compete with larger general models. Secondly, the study evaluated a proposed on-device self-supervised labeling strategy that integrates SAHI with a bidirectional implementation of ByteTrack. The proposed self-supervised labeling strategy provided reliable performance gains across all architectures and configurations, by recovering hard negatives, more specifically small, occluded and low confidence instances. Thirdly, the study investigated background-context fusion (BF). It proved to be consistently improving the performance in general for RFDETR, while it proved inconsistent for YOLO11 and failed to increase robustness against seasonality, suggesting it induced background-dependent overfitting. Finally, the study shows that all models being trained on a summer scene exhibit a decrease in relative performance compared with the non-adapted models during a seasonal domain shift to a winter scene.