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Oyster mushrooms can be cultivated with great spatial efficiency, on nutrient-poor plant materials, without light and under diverse climatic conditions. Their production therefore has a great potential for improving food security, especially in impoverished and overpopulated areas. However, the pasteurization or sterilization of mushroom substrates uses a lot of energy and water. This study investigates the impact of different pasteurization and sterilization techniques on the growth and yield of oyster mushrooms, and evaluates their water and energy usage. The efficacy of heat-based methods, including hot water, hot air, and pressurized steam, as well as a chemical method utilizing hydrated lime (Ca(OH)2) were assessed. The results show that sterilizing mushroom substrates through autoclaving can significantly increase the dry yields, up to 50% compared to pasteurization methods. However, pasteurization methods also achieved excellent results compared to untreated substrates, with good harvests and low pest occurrence. The mushroom water content was significantly higher in pasteurization methods where the substrate is submerged in water. In terms of fresh yield, hot water pasteurization was as good as autoclaving and significantly better than the other pasteurization methods. Hot air pasteurization has, on balance, a better water and energy efficiency than autoclave sterilization (about 75 % less energy) or hot water pasteurization (about 85 % less water). When performed at an air temperature of 75° C, which was found to be sufficient for successful mushroom cultivation, as little as 1068 kJ was needed to pasteurize one kg of dry substrate (e.g. maize straw). While hydrated lime pasteurization could use as little as 270 kJ per kg of dry substrate, it is very wasteful of water, as is hot water pasteurization and could lead to nutrient leaking. The success of mushroom cultivation, especially with hot air pasteurization, could also be influenced by the duration of sub

Motion prediction and planning are key components to enable autonomous driving. Although high definition (HD) maps provide important contextual information that constrains the action space of traffic participants, most approaches are not able to fully exploit this heterogeneous information. In this work, we enrich the existing road geometry of the popular nuScenes dataset and convert it into the open-source map framework Lanelet2. This allows easy access to the road topology and thus, enables the usage of (1) spatial semantic information, such as agents driving on intersecting roads and (2) map-generated anchor paths for target vehicles that can help to improve trajectory prediction performance. Further, we present DMAP, a simple, yet effective approach for diverse map-based anchor path generation and filtering. We show that combining DMAP with ground truth velocity profile information yields high-quality motion prediction results on nuScenes (MinADE5=1.09, MissRate5,2=0.18, Offroad rate=0.00). While it is obviously unfair to compare us against the state-of-the-art, it shows that our HD map accurately depicts the road geometry and topology. Future approaches can leverage this by focusing on data-driven sampling of map-based anchor paths and estimating velocity profiles. Moreover, our HD map can be used for map construction tasks and supplement perception. Code and data are made publicly available at https://felixhertlein.github.io/lanelet4nuscenes., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Vehicles

Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between traffic agents, map context and traffic rules. Vector-based approaches have recently shown to achieve among the best performances on trajectory prediction benchmarks. These methods model simple interactions between traffic agents but don't distinguish between relation-type and attributes like their distance along the road. Furthermore, they represent lanes only by sequences of vectors representing center lines and ignore context information like lane dividers and other road elements. We present a novel approach for vector-based trajectory prediction that addresses these shortcomings by leveraging three crucial sources of information: First, we model interactions between traffic agents by a semantic scene graph, that accounts for the nature and important features of their relation. Second, we extract agent-centric image-based map features to model the local map context. Finally, we generate anchor paths to enforce the policy in multi-modal prediction to permitted trajectories only. Each of these three enhancements shows advantages over the baseline model HoliGraph., Comment: Accepted on IEEE ITSC 2023