Journal Article10.1162/089976699300016890
Modeling and prediction of human behavior
Alex Pentland,Andrew Liu +1 more
TL;DR: This work proposes that many human behaviors can be accurately described as a set of dynamic models sequenced together by a Markov chain and uses these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time.
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Abstract: We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements. Language: en
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Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network
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Intention Prediction and Mixed Strategy Nash Equilibrium-Based Decision-Making Framework for Autonomous Driving in Uncontrolled Intersection
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Intelligent erratic driving diagnosis based on artificial neural networks
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A Bayesian inference based adaptive lane change prediction model
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TL;DR: Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL), achieving high recognition rates for full sentence ASL using only visual cues.
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