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Probabilistic and optimal Human Navigational Intent Prediction

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2022-08-23
Authors/Contributors
Abstract
The ability to predict human behaviour and choose the right action for a robot is crucial to safe and natural operation in an environment shared with humans. Assuming a probabilistic model for human navigational decision-making, in this thesis, we aim to tackle the problem of Human Navigational Intent Prediction. To this end, we propose a probabilistic framework for fast and accurate estimation of the probability distribution over future human states given the previous state. We introduce three internal parameters to our model: the human goal (g), the optimality of human actions (β), and farsightedness (γ). Our framework maintains and updates a belief over the parameters by observing human actions online. Also, in contrast to existing methods, we consider a 4D state for human dynamics. We overcome the challenges introduced by using a more complex model by precomputation of a Time-To-Reach based value function and exploiting particle filter sampling for human states and the goal distribution. We evaluated our method using synthetically generated and real-world data and have shown that our method outperforms the baseline in longer prediction horizons.
Document
Extent
32 pages.
Identifier
etd22127
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Chen, Mo
Language
English
Member of collection
Download file Size
etd22127.pdf 5.88 MB

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