Automated and Autonomous Driving in Freight Transport - Opportunities and Limitations
28 Sep 2020-pp 457-475
TL;DR: The discussions regarding the existing automated and autonomous vehicles in the field of the main freight transport modes as well as possible developments will be presented and considered in the light of future demand structures.
Abstract: The development of mobility has always had a considerable influence on economic, social and political structures. Without efficient transport systems, the industrial revolutions of the last centuries would not have been possible or only to a much lesser extent. With the advancing digitalization and the development of automated and autonomous vehicles, new framework conditions are emerging, which are leading to far-reaching changes in the transport sector. In this contribution, the discussions regarding the existing automated and autonomous vehicles in the field of the main freight transport modes as well as possible developments will be presented and considered in the light of future demand structures.
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26 Sep 2021
TL;DR: In this paper, the authors benchmark interpretable and non-interpretable transfer learning for human activity recognition on the LARa order-picking dataset with both domain-related and domain-foreign reference datasets.
Abstract: Human activity recognition evolves around classifying and analyzing workers’ actions quantitatively using convolutional neural networks on the time-series data provided by inertial measurement units and motion capture systems. However, this requires expensive training datasets since each warehouse scenario has slightly different settings and activities of interest. Here, transfer learning promises to shift the knowledge a deep learning method gained on existing reference data to new target data. We benchmark interpretable and non-interpretable transfer learning for human activity recognition on the LARa order-picking dataset with AndyLab and RealDisp as domain-related and domain-foreign reference datasets. We find that interpretable transfer learning via the recently proposed probabilistic rule stacking learner, which does not require any labeled data on the target dataset, is possible if the labels are sufficiently semantically related. The success depends on the proximity of the reference and target domains and labels. Non-interpretable transfer learning via fine-tuning can be applied even if there is a major domain-shift between the datasets and reduces the amount of labeled data required on the target dataset.
4 citations
01 Jan 2019
TL;DR: In this paper, the authors present the significance and potential of EU inland waterway transport, relevant sources of law and their implementation, as well as activities and results of work in the field of inland Waterway transport.
Abstract: Although the inland waterways transport (transport/navigation on rivers, lakes and canals) is, by its characteristics, an acceptable mode of transport, in some EU member states it is still unfortunately underestimated and insufficiently exploited. Cost- effectiveness, safety and eco-friendliness are the main characteristics of the inland waterway transport. The European Union emphasizes the reduction of traffic congestion, increased traffic safety, the use of environmentally friendly modes of transport and the use of alternative fuels as the goals of its transport policy. Developed inland waterway transport is therefore one of the possible solutions for the achievement of these goals. European Union has some 40, 000 km of navigable waterways and 13 Member States have an interconnected waterway network, which is a great potential for the development of this mode of transport. Although the European Union has provided a number of programmes and actions for inland waterway transport to encourage it and to increase the share of goods and passengers transported by inland waterways, the major results appear to be absent. The paper presents the significance and potential of EU inland waterway transport, relevant sources of law and their implementation, as well as activities and results of work in the field of inland waterway transport.
1 citations
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