17,832 research outputs found
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
In this work we anticipate driving maneuvers a few seconds before they occur.
For this purpose we equip a car with cameras and a computing device to capture
the driving context from both inside and outside of the car. We propose an
Autoregressive Input-Output HMM to model the contextual information alongwith
the maneuvers. We evaluate our approach on a diverse data set with 1180 miles
of natural freeway and city driving and show that we can anticipate maneuvers
3.5 seconds before they occur with over 80\% F1-score in real-time.Comment: ICCV 2015, http://brain4cars.co
Anticipating Visual Representations from Unlabeled Video
Anticipating actions and objects before they start or appear is a difficult
problem in computer vision with several real-world applications. This task is
challenging partly because it requires leveraging extensive knowledge of the
world that is difficult to write down. We believe that a promising resource for
efficiently learning this knowledge is through readily available unlabeled
video. We present a framework that capitalizes on temporal structure in
unlabeled video to learn to anticipate human actions and objects. The key idea
behind our approach is that we can train deep networks to predict the visual
representation of images in the future. Visual representations are a promising
prediction target because they encode images at a higher semantic level than
pixels yet are automatic to compute. We then apply recognition algorithms on
our predicted representation to anticipate objects and actions. We
experimentally validate this idea on two datasets, anticipating actions one
second in the future and objects five seconds in the future.Comment: CVPR 201
Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction
Close human-robot cooperation is a key enabler for new developments in
advanced manufacturing and assistive applications. Close cooperation require
robots that can predict human actions and intent, and understand human
non-verbal cues. Recent approaches based on neural networks have led to
encouraging results in the human action prediction problem both in continuous
and discrete spaces. Our approach extends the research in this direction. Our
contributions are three-fold. First, we validate the use of gaze and body pose
cues as a means of predicting human action through a feature selection method.
Next, we address two shortcomings of existing literature: predicting multiple
and variable-length action sequences. This is achieved by introducing an
encoder-decoder recurrent neural network topology in the discrete action
prediction problem. In addition, we theoretically demonstrate the importance of
predicting multiple action sequences as a means of estimating the stochastic
reward in a human robot cooperation scenario. Finally, we show the ability to
effectively train the prediction model on a action prediction dataset,
involving human motion data, and explore the influence of the model's
parameters on its performance. Source code repository:
https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018,
Accepte
Embodied Cognition and Perception: Dewey, Science and Skepticism
This article examines how Modern theories of mind remain even in some materialistic and hence ontologically anti-dualistic views; and shows how Dewey, anticipating Merleau-Ponty and 4E cognitive scientists, repudiates these theories. Throughout I place Dewey’s thought in the context of scientific inquiry, both recent and historical and including the cognitive as well as traditional sciences; and I show how he incorporated sciences of his day into his thought, while also anticipating enactive cognitive science. While emphasizing Dewey’s continued relevance, my main goal is to show how his scientifically informed account of perception and cognition combats skepticism propagated by certain scientific visions, exacerbated by commonplace notions about mind, that jointly suggest that human beings lack genuine access to reality
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