21,994 research outputs found
An ontology-based approach to relax traffic regulation for autonomous vehicle assistance
Traffic regulation must be respected by all vehicles, either human- or
computer- driven. However, extreme traffic situations might exhibit practical
cases in which a vehicle should safely and reasonably relax traffic regulation,
e.g., in order not to be indefinitely blocked and to keep circulating. In this
paper, we propose a high-level representation of an automated vehicle, other
vehicles and their environment, which can assist drivers in taking such
"illegal" but practical relaxation decisions. This high-level representation
(an ontology) includes topological knowledge and inference rules, in order to
compute the next high-level motion an automated vehicle should take, as
assistance to a driver. Results on practical cases are presented
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
We present a probabilistic generative model for inferring a description of
coordinated, recursively structured group activities at multiple levels of
temporal granularity based on observations of individuals' trajectories. The
model accommodates: (1) hierarchically structured groups, (2) activities that
are temporally and compositionally recursive, (3) component roles assigning
different subactivity dynamics to subgroups of participants, and (4) a
nonparametric Gaussian Process model of trajectories. We present an MCMC
sampling framework for performing joint inference over recursive activity
descriptions and assignment of trajectories to groups, integrating out
continuous parameters. We demonstrate the model's expressive power in several
simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event
video data sets.Comment: 10 pages, 6 figures, in Proceedings of the 30th AAAI Conference on
Artificial Intelligence (AAAI'16), Phoenix, AZ, 201
Adaptive Probability Theory: Human Biases as an Adaptation
Humans make mistakes in our decision-making and probability judgments. While the heuristics used for decision-making have been explained as adaptations that are both efficient and fast, the reasons why people deal with probabilities using the reported biases have not been clear. We will see that some of these biases can be understood as heuristics developed to explain a complex world when little information is available. That is, they approximate Bayesian inferences for situations more complex than the ones in laboratory experiments and in this sense might have appeared as an adaptation to those situations. When ideas as uncertainty and limited sample sizes are included in the problem, the correct probabilities are changed to values close to the observed behavior. These ideas will be used to explain the observed weight functions, the violations of coalescing and stochastic dominance reported in the literature
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