Bayesian Human Motion Intentionality Prediction in urban environments

Bayesian Human Motion Intentionality Prediction in urban environments

Human motion prediction in indoor and outdoor scenarios is a key issue towards human robot interaction and intelligent robot navigation in general. In the present work, we propose a new human motion intentionality indicator, denominated Bayesian Human Motion Intentionality Prediction (BHMIP), which is a geometric-based long-term predictor. Two variants of the Bayesian approach are proposed, the...

List of changes of the article “ Bayesian human motion intentionality prediction in urban environments ”

Human motion prediction in indoor and outdoor scenarios is a key issue towards human robot interaction and intelligent robot navigation in general. In the present work, we propose a new human motion intentionality indicator, denominated Bayesian Human Motion Intentionality Prediction (BHMIP), which is a geometric-based long-term predictor. Two variants of the Bayesian approach are proposed, the...

Motion Planning in Urban Environments

We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultrareliability, high-speed operation, complex intervehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for c...

Terrorist Threat Prediction in Urban Environments

Terrorist events, such as suicide bombings, are frightening, destructive, and rare. The nature of suicide bombings and improved explosive device (IED) attacks makes effective defensive measures difficult with the result that defensive actions typically impose greater restrictions on the larger population. Predicting the locations of terrorist events can enable more directed defensive efforts. W...

Bayesian Prediction in Spatial Data Analysis