Anton Pollak

Computational defogging using machine learning presents significant potential; however, its progress is hindered by the scarcity of large-scale datasets comprising real-world paired images with sufficiently dense fog. To address this limitation, we developed a binocular imaging system and introduced Stereofog—an open-source dataset comprising 10,067 paired clear and foggy images, with a majority captured under dense fog conditions. Utilizing this dataset, we trained a pix2pix image-to-image (I2I) translation model and achieved a complex wavelet structural similarity index (CW-SSIM) exceeding 0.7 and a peak signal-to-noise ratio (PSNR) above 17, specifically under dense fog conditions (characterized by a Laplacian variance, v_L < 10). We note that Stereofog contains over 70% of dense-fog images. In contrast, models trained on synthetic data, or real-world images augmented with synthetic fog, exhibited suboptimal performance. Our comprehensive performance analysis highlights the model’s limitations, such as issues related to dataset diversity and hallucinations—challenges that are pervasive in machine-learning-based approaches. We also propose several strategies for future improvements. Our findings emphasize the promise of machine-learning techniques in computational defogging across diverse fog conditions. This work contributes to the field by offering a robust, open-source dataset that we anticipate will catalyze advancements in both algorithm development and data acquisition methodologies.

Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy-duty trucks, and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e.g., the conversion and storage of surplus renewable electricity via electrolysis, become important.

Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel-cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy duty trucks and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e. g. the conversion and storage of surplus renewable electricity via electrolysis, become important.

Autonomous smart waste management services are becoming an essential component of sustainable urbanization. However, the lack of data and insights from current service-providers impedes a reliable transition from labor-intensive to autonomous services. Deploying information gathering devices makes services expensive and resource-demanding. In project MARBLE (Mobile Autonomous RoBot for Litter Emptying) we are currently investigating the implementation of a fleet of service robots. In this framework, we could show that the absence of filling data of litter bins (LBs) hinders the possibility of providing an energy-efficient and time-effective service. Hence, we introduce an approach where machine learning-based predictions for filling levels of LBs, derived from our extensive data gathering, are used to effectively manage the autonomous emptying process. The novel Simulated Rebalancing approach in route-planning combined with the Knapsack algorithm ensures efficient service in comparison to the Nearest Neighbor algorithm. A promising 82% filling level prediction accuracy was achieved with the XGBoost binary classifier, as compared to the 59% baseline accuracy. Through incorporating the predicted filling level data in the Simulated Rebalancing approach, a reduction of 26% in operational time and 31% in energy consumption was achieved for our simulated tests for service-event-area (SEA) James-Simon-Monbijoupark in Berlin with 49 LBs.