3 research outputs found
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an
important role in future traffic. To avoid accidents and achieve a highly
efficient traffic flow, it is important to detect VRUs and to predict their
intentions. In this article a holistic approach for detecting intentions of
VRUs by cooperative methods is presented. The intention detection consists of
basic movement primitive prediction, e.g. standing, moving, turning, and a
forecast of the future trajectory. Vehicles equipped with sensors, data
processing systems and communication abilities, referred to as intelligent
vehicles, acquire and maintain a local model of their surrounding traffic
environment, e.g. crossing cyclists. Heterogeneous, open sets of agents
(cooperating and interacting vehicles, infrastructure, e.g. cameras and laser
scanners, and VRUs equipped with smart devices and body-worn sensors) exchange
information forming a multi-modal sensor system with the goal to reliably and
robustly detect VRUs and their intentions under consideration of real time
requirements and uncertainties. The resulting model allows to extend the
perceptual horizon of the individual agent beyond their own sensory
capabilities, enabling a longer forecast horizon. Concealments,
implausibilities and inconsistencies are resolved by the collective
intelligence of cooperating agents. Novel techniques of signal processing and
modelling in combination with analytical and learning based approaches of
pattern and activity recognition are used for detection, as well as intention
prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge
fusion, takes place on the level of perception and intention recognition. Based
on the requirements of the cooperative approach for the communication a new
strategy for an ad hoc network is proposed.Comment: 20 pages, published at Automatisiertes und vernetztes Fahren (AAET),
Braunschweig, Germany, 201
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
In recent years, transfer learning gained particular interest in the field of
vision and natural language processing. In the research field of vision, e.g.,
deep neural networks and transfer learning techniques achieve almost perfect
classification scores within minutes. Nonetheless, these techniques are not yet
widely applied in other domains. Therefore, this article identifies critical
challenges and shows potential solutions for power forecasts in the field of
renewable energies. It proposes a framework utilizing transfer learning
techniques in wind power forecasts with limited or no historical data. On the
one hand, this allows evaluating the applicability of transfer learning in the
field of renewable energy. On the other hand, by developing automatic
procedures, we assure that the proposed methods provide a framework that
applies to domains in organic computing as well
Extended Coopetitive Soft Gating Ensemble
This article is about an extension of a recent ensemble method called
Coopetitive Soft Gating Ensemble (CSGE) and its application on power
forecasting as well as motion primitive forecasting of cyclists. The CSGE has
been used successfully in the field of wind power forecasting, outperforming
common algorithms in this domain. The principal idea of the CSGE is to weight
the models regarding their observed performance during training on different
aspects. Several extensions are proposed to the original CSGE within this
article, making the ensemble even more flexible and powerful. The extended CSGE
(XCSGE as we term it), is used to predict the power generation on both wind-
and solar farms. Moreover, the XCSGE is applied to forecast the movement state
of cyclists in the context of driver assistance systems. Both domains have
different requirements, are non-trivial problems, and are used to evaluate
various facets of the novel XCSGE. The two problems differ fundamentally in the
size of the data sets and the number of features. Power forecasting is based on
weather forecasts that are subject to fluctuations in their features. In the
movement primitive forecasting of cyclists, time delays contribute to the
difficulty of the prediction. The XCSGE reaches an improvement of the
prediction performance of up to 11% for wind power forecasting and 30% for
solar power forecasting compared to the worst performing model. For the
classification of movement primitives of cyclists, the XCSGE reaches an
improvement of up to 28%. The evaluation includes a comparison with other
state-of-the-art ensemble methods. We can verify that the XCSGE results are
significantly better using the Nemenyi post-hoc test.Comment: 14 pages; 15 figures; 10 table