16,560 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Using a Machine Learning Approach to Implement and Evaluate Product Line Features
Bike-sharing systems are a means of smart transportation in urban
environments with the benefit of a positive impact on urban mobility. In this
paper we are interested in studying and modeling the behavior of features that
permit the end user to access, with her/his web browser, the status of the
Bike-Sharing system. In particular, we address features able to make a
prediction on the system state. We propose to use a machine learning approach
to analyze usage patterns and learn computational models of such features from
logs of system usage.
On the one hand, machine learning methodologies provide a powerful and
general means to implement a wide choice of predictive features. On the other
hand, trained machine learning models are provided with a measure of predictive
performance that can be used as a metric to assess the cost-performance
trade-off of the feature. This provides a principled way to assess the runtime
behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338
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