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Titlebook: Incremental Learning for Motion Prediction of Pedestrians and Vehicles; Alejandro Dizan Vasquez Govea Book 2010 Springer-Verlag Berlin Hei

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21#
發(fā)表于 2025-3-25 04:38:51 | 只看該作者
Probabilistic Modelsble to begin with a summary introduction to the subject. This chapter is strongly based on Lebeltel [1999], the interested reader is also invited to consult the works of Stone et al. [1999] and Thrun et al. [2005] for a more thorough explanation of the concepts presented here.
22#
發(fā)表于 2025-3-25 10:20:29 | 只看該作者
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發(fā)表于 2025-3-25 14:55:06 | 只看該作者
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發(fā)表于 2025-3-25 19:16:14 | 只看該作者
25#
發(fā)表于 2025-3-25 23:48:47 | 只看該作者
https://doi.org/10.1007/978-3-642-13642-9Hidden Markov Models; Motion prediction; behaviour modelling; hidden markov model; machine learning; robo
26#
發(fā)表于 2025-3-26 03:40:39 | 只看該作者
27#
發(fā)表于 2025-3-26 06:59:40 | 只看該作者
28#
發(fā)表于 2025-3-26 12:07:19 | 只看該作者
Experimental Resultsions. Next, we will introduce the methodology and present the results of comparing the proposed approach against two other state-of-the-art techniques. Finally, we will close the chapter with a general discussion of the results.
29#
發(fā)表于 2025-3-26 16:35:15 | 只看該作者
1610-7438 on the winning thesis of the EURON Georges Giralt award.Modeling and predicting human and vehicle motion is an active research domain.Owing to the difficulty in modeling the various factors that determine motion(e.g. internal state, perception) this is often tackled by applying machinelearning techn
30#
發(fā)表于 2025-3-26 20:49:47 | 只看該作者
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