5,490 research outputs found
RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
A key aspect of driving a road vehicle is to interact with the other road
users, assess their intentions and make risk-aware tactical decisions. An
intuitive approach of enabling an intelligent automated driving system would be
to incorporate some aspects of the human driving behavior. To this end, we
propose a novel driving framework for egocentric views, which is based on
spatio-temporal traffic graphs. The traffic graphs not only model the spatial
interactions amongst the road users, but also their individual intentions
through temporally associated message passing. We leverage spatio-temporal
graph convolutional network (ST-GCN) to train the graph edges. These edges are
formulated using parameterized functions of 3D positions and scene-aware
appearance features of road agents. Along with tactical behavior prediction, it
is crucial to evaluate the risk assessing ability of the proposed framework. We
claim that our framework learns risk aware representations by improving on the
task of risk object identification, especially in identifying objects with
vulnerable interactions like pedestrians and cyclists
A study in the cognition of individuals’ identity: Solving the problem of singular cognition in object and agent tracking
This article compares the ability to track individuals lacking mental states with the ability to track intentional agents. It explains why reference to individuals raises the problem of explaining how cognitive agents track unique individuals and in what sense reference is based on procedures of perceptual-motor and epistemic tracking. We suggest applying the notion of singular-files from theories in perception and semantics to the problem of tracking intentional agents. In order to elucidate the nature of agent-files, three views of the relation between object- and agent-tracking are distinguished: the Independence, Deflationary and Organism-Dependence Views. The correct view is argued to be the latter, which states that perceptual and epistemic tracking of a unique human organism requires tracking both its spatio-temporal object-properties and its agent-properties
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
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