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Senast publicerade

Development environments for route based functions in road vehicles
(2026) Andersson, Victor
The electrification of heavy vehicles has increased demands on accurate range estimation to support efficient transport planning and reduce range anxiety. Consequently, route-based predictive energy estimation has become an important tool for improving the reliability of range predictions. The energy consumption of heavy vehicles is strongly influenced by factors such as vehicle mass, route topography, traffic conditions, and driving behaviour. This thesis investigates a predictive range estimation development environment for battery-electric heavy vehicles using a Kalman filter-based energy estimator integrated with TruckMaker simulations and MATLAB-based post-processing. The estimator combines route, weather, and mission information to predict battery energy consumption, state of charge (SOC), and remaining driving range together with its associated variance along a predefined route between the Port of Gothenburg and Viared. The accuracy of the estimator was evaluated by comparing predicted vehicle behaviour with TruckMaker simulation results. In addition, the influence of re-estimation frequency, vehicle mass uncertainty, and traffic disturbances was investigated. Several distance-based re-estimation intervals were analysed to evaluate how periodic updates affect prediction accuracy. Furthermore, scenarios involving incorrect vehicle mass information, mid-route mass correction, and traffic congestion were introduced to assess the robustness of the prediction framework under changing operating conditions. The results show that the estimator is capable of predicting SOC and vehicle range with good accuracy under nominal conditions. Periodic re-estimation significantly improves prediction accuracy and enables the estimator to adapt to changing operating conditions throughout the route. Vehicle mass was identified as one of the most influential parameters affecting prediction accuracy, while the investigated traffic disturbance had a comparatively smaller impact on the overall energy consumption. The driver-related estimator parameters K and σv,ref were also investigated. Both parameters influenced the predicted mean energy consumption and the associated uncertainty, while updating them to values identified from previously driven data resulted in only minor changes in the SOC prediction. These results indicate that the parameters may be useful for tuning the predicted mean energy demand and its associated uncertainty. Overall, the results indicate that continuous re-estimation can improve the robustness of route-based range prediction for battery-electric heavy vehicles.
Hälsa och Säkerhet - Reducera tunga lyft av tvättsäckar för lokalvårdare på Östra Sjukhuset
(2025) Al Masri , Yousef; Friberg, Jakob; Jirdén, Tora; Jönsson , Moa; Löwenberg , David; Sahlqvist, Johan
This bachelor thesis explores how user-centered design with focus on ergonomics can contribute to a safer handling of lifting heavy laundry bags for environmental service workers in their daily work. The aim of the project is to reduce the risk of injury when lifting laundry bags into a laundry cart for environmental service workers at Östra Sjukhuset during clearance of laundry from the healthcare personnel’s locker rooms. The project has been executed through an iterative design process with interviews, ideation, sketches, concept development, prototyping and user tests. Initially, information gathering and analysis of the current conditions were carried out to identify the target group's needs. From the result of these, the process continued further until ideation and concept development where a number of concepts were produced. The concepts were evaluated and refined, then prototypes were built for the three further developed concepts which were presented to the client. The final result was two of the concepts in combination with each other where one concept distributes the weight more equally between laundry bags and the other assists in lifting said laundry bags. The conclusion is that the developed concept fulfills the purpose of the project and that further work in the area is advocated. It is suggested that future evaluations be carried out to complete the concept further with calculations and technical sketches.
Synthetic Data Generation for Industrial Perception Using 3DGS
(2026) Gurusinghe, Dineth; Khalid, Yahya
Autonomous mobile robots operating in industrial environments require reliable camera-based perception to detect pedestrians, forklifts, and other vehicles in dynamic shared workspaces. However, collecting and manually annotating sufficiently large and diverse real-world datasets in factory environments is costly and timeconsuming. This thesis investigates the use of 3D Gaussian Splatting (3DGS) as the basis of a synthetic-data-generation pipeline for training industrial object-detection models and examines factors affecting simulation-to-reality transfer. A 3DGS reconstruction of a real factory environment was integrated into Unreal Engine as a digital-twin background and combined with dynamic simulated actors representing pedestrians, forklifts, and other vehicles. Synthetic images and corresponding object-detection annotations were generated from multiple virtual camera viewpoints and compiled into datasets for training YOLOv8-based detection models. A model trained on a 21-camera synthetic dataset achieved an mAP50 of 0.971 on synthetic test data but only 0.227 on real factory images, demonstrating a substantial simulation-to-reality domain gap. Experiments showed that both camera-viewpoint diversity and environmental representation affected real-world generalisation. Models trained using the 3DGS factory representation transferred better to real images than those trained using a conventional Unreal Engine environment. However, because the appearance encoded by 3DGS is tied to the lighting conditions under which the scene was captured, lighting differences could not be varied independently, limiting the extent to which this improvement could be attributed solely to the background representation. Diffusionbased style transfer substantially improved zero-shot real-domain performance, increasing mAP50 from 0.125 to 0.440, while subsequent pseudo-labelling increased it further to 0.504 without requiring manually annotated real training images. Synthetic pre-training followed by fine-tuning on real data increased mean mAP50 from 0.876 to 0.891 compared with real-only fine-tuning, although this difference fell within the observed variability across data splits. In contrast, joint training with style-adapted synthetic and real data produced more substantial gains when only limited annotated real data was available. Overall, the results indicate that 3DGSbased synthetic data is most valuable as a complement to real-world data rather than as a replacement for it, particularly when viewpoint diversity, visual domain alignment, and training strategy are considered together.
Falsification of Safety-Critical Decision and Control Software
(2026) Zhang, Pingjie; Zhang, Yang
Safety testing of autonomous driving systems requires methods that can expose rare but safety-critical failures beyond nominal driving behavior. Since exhaustive realworld testing is infeasible, simulation-based falsification provides a practical way to actively search for scenarios in which a system under test violates desired safety requirements. This thesis investigates deep reinforcement learning for falsification of safety-critical decision and control software in closed-loop simulation. The falsification task is formulated as an adversarial control problem in a simplified longitudinal two-vehicle scenario, where a learning agent controls the target vehicle and attempts to induce collisions with an ego vehicle controlled by the system under test. A TD3- based falsifier is first evaluated against Monte Carlo Tree Search and uniform random sampling to study collision discovery performance across randomly sampled initial conditions. The thesis then extends the framework towards diversity-oriented falsification using a DTW-based(dynamic time wrapping) Series RL method. In this method, previously discovered collision trajectories are stored in a repository and used to reward later agents for finding trajectories with different gap and relativespeed evolution. A collision replay buffer is also introduced to increase the reuse of rare collision-related experiences during training. The results show that the TD3- based falsifier discovers collisions more effectively than the non-learning baselines in the tested setting. They also show that the DTW-based diversity reward leads to a broader and less repetitive set of collision trajectories than the baseline Series RL method. These findings suggest that reinforcement learning can support both efficient failure discovery and diversity-oriented exploration of failure modes, although the conclusions are limited to the simplified simulation setting considered in this thesis.
Vibration Condition Monitoring of an Electric Drive Unit
(2026) Nalawade, Aditya
The increasing competition and rapid development in electromobility are driving accelerated product development, higher drivetrain performance, and consequently greater mechanical stress on components. Ensuring reliability and safety therefore requires effective condition monitoring during testing. Vibration-based condition monitoring is widely used for this purpose; however, the effectiveness of such monitoring strongly depends on the measurement setup, particularly sensor placement. In practice, sensor positioning is often performed without a structured approach, which could lead to inconsistent and potentially unreliable results. This study focuses on developing a standardized procedure for vibration measurement setup during drivetrain testing. Multiple real-time measurements were conducted and analysed using various evaluation metrics to assess the influence of sensor placement and operating conditions. The results demonstrate that sensor placement must be carefully optimised for different machine configurations. In addition, the classification of load cases requires refinement to support a consistent and standardized testing methodology. The proposed approach contributes to improved reliability and comparability of condition monitoring results.