109,353 research outputs found
Three levels of metric for evaluating wayfinding
Three levels of virtual environment (VE) metric are proposed, based on: (1) users’ task performance (time taken, distance traveled and number of errors made), (2) physical behavior (locomotion, looking around, and time and error classification), and (3) decision making (i.e., cognitive) rationale (think aloud, interview and questionnaire). Examples of the use of these metrics are drawn from a detailed review of research into VE wayfinding. A case study from research into the fidelity that is required for efficient VE wayfinding is presented, showing the unsuitability in some circumstances of common metrics of task performance such as time and distance, and the benefits to be gained by making fine-grained analyses of users’ behavior. Taken as a whole, the article highlights the range of techniques that have been successfully used to evaluate wayfinding and explains in detail how some of these techniques may be applied
Evaluation of estimation approaches on the quality and robustness of collision warning system
Vehicle safety is one of the most challenging aspect of future-generation
autonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as a
proposed solution framework, can be relied as the main structure to address the
issues in this area. In this framework, information plays a very important
role. Each vehicle has access to its own information immediately. However,
another vehicle information is available through a wireless communication. Data
loss is very common issue for such communication approach. As a consequence,
CCW would suffer from providing late or false detection awareness. Robust
estimation of lost data is of this paper interest which its goal is to
reconstruct or estimate lost network data from previous available or estimated
data as close to actual values as possible under different rate of lost. In
this paper, we will investigate and evaluate three different algorithms
including constant velocity, constant acceleration and Kalman estimator for
this purpose. We make a comparison between their performance which reveals the
ability of them in term of accuracy and robustness for estimation and
prediction based on previous samples which at the end affects the quality of
CCW in awareness generation
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
Reformulating computer vision problems over Riemannian manifolds has
demonstrated superior performance in various computer vision applications. This
is because visual data often forms a special structure lying on a lower
dimensional space embedded in a higher dimensional space. However, since these
manifolds belong to non-Euclidean topological spaces, exploiting their
structures is computationally expensive, especially when one considers the
clustering analysis of massive amounts of data. To this end, we propose an
efficient framework to address the clustering problem on Riemannian manifolds.
This framework implements random projections for manifold points via kernel
space, which can preserve the geometric structure of the original space, but is
computationally efficient. Here, we introduce three methods that follow our
framework. We then validate our framework on several computer vision
applications by comparing against popular clustering methods on Riemannian
manifolds. Experimental results demonstrate that our framework maintains the
performance of the clustering whilst massively reducing computational
complexity by over two orders of magnitude in some cases
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