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Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network
2020
ISPRS International Journal of Geo-Information
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Accurate and timely location prediction of moving objects is crucial for intelligent transportation systems and traffic management. ...
Motivated by the current study status, we are dedicated to a deep-learning-based approach to predict the coordinates of several future locations of moving objects based on recent trajectory records. ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/ijgi9020116
fatcat:ta6kqmolujfbfmna6svygebrku
Modelling pedestrian trajectory patterns with Gaussian processes
2009
2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
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We illustrate the benefit of this approach for long term motion prediction where parametric models such as Kalman Filters would perform poorly. ...
We propose a non-parametric model for pedestrian motion based on Gaussian Process regression, in which trajectory data are modelled by regressing relative motion against current position. ...
Long term prediction can therefore be used to aid in target reacquisition. ...
doi:10.1109/iccvw.2009.5457470
dblp:conf/iccvw/EllisS009
fatcat:twq3a2i4c5ezrejd65rdbpymfi
Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model
2009
Ecological Modelling
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For cows' movement between the "stay" areas a long-term prediction algorithm was implemented. ...
Two learning algorithms were implemented: a Hidden Markov Model (HMM) and a long-term prediction-learning algorithm. ...
The path by which the cow moved from one stay region to another was 18 generated using a long-term prediction learning process. 19 20 More details on the HMM design, the long-term prediction learning ...
doi:10.1016/j.ecolmodel.2009.04.047
fatcat:o44tm4lkgzdp7nsmjiuf5mnoda
A Mnemonic Kalman Filter for Non-Linear Systems with Extensive Temporal Dependencies
2020
IEEE Signal Processing Letters
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Its true motion might depend on hundreds of parameters and can involve long-term temporal correlation. ...
In particular, the Kalman Filter assumes prior and posterior Gaussian densities and is hence restricted to linear transition functions which are often insufficient to reflect the behaviour of a real object ...
This Multivariate Density Long Short-Term Memory (MD-LSTM) network is then used in the KF prediction step to estimate the future state of a single object along with its covariance (Section III-B). ...
doi:10.1109/lsp.2020.3000679
fatcat:2hjpd7iwfza4pa5mnqmvrbrzsa
Motion prediction for moving objects: a statistical approach
2004
IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
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This paper proposes a technique to obtain long term estimates of the motion of a moving object in a structured environment. ...
The results show that the technique is general, produces long-term predictions and is fast enough for its use in real time applications. ...
Since they permit to take into account not only the current state of the object but also its past states, clusterbased techniques are by far the best ones when it comes to long term motion prediction. ...
doi:10.1109/robot.2004.1308883
dblp:conf/icra/VasquezF04
fatcat:hzmz4b6lkrhxlaeurjdq27xf3q
Predictive Navigation by Understanding Human Motion Patterns
2011
International Journal of Advanced Robotic Systems
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To make robots coexist and share the environments with humans, robots should understand the behaviors or the intentions of humans and further predict their motions. ...
Finally, the simulations and experiments are shown to validate the idea of this paper. ...
Formulation of Prediction In prediction process, we split the problem into short term and long term prediction. ...
doi:10.5772/10529
fatcat:fo2jlr5w5fhebm3lkbkz63ekia
I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction
[article]
2017
arXiv
pre-print
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We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments. ...
We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. ...
The Gaussian Process regression algorithm corresponds to using Gaussian distribution ellipsoids around the predicted mean values of the joint positions. ...
arXiv:1608.04837v5
fatcat:64ygzlhu6fcrrjkqtlaf23xsju
Prediction-based Online Trajectory Compression
[article]
2016
arXiv
pre-print
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ONTRAC learns prediction models for suppressing updates to a trajectory database using training data. ...
Recent spatio-temporal data applications, such as car-shar\-ing and smart cities, impose new challenges regarding the scalability and timeliness of data processing systems. ...
Scalability is measured in terms of the number of inserts processed per second and querying performance is evaluated in terms of the number of where (Definition 3) queries processed per second. ...
arXiv:1601.06316v2
fatcat:wpyhhypirbfqlhuzltzp55arw4
Vehicle Motion Prediction Algorithm with Driving Intention Classification
2022
Applied Sciences
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based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive ...
the future trajectory of the leading vehicle. ...
In [10] , LSTM (long short-term memory) is used to learn the mapping from the input of the original sensor to the object coordinates in an unsupervised manner. ...
doi:10.3390/app12157443
fatcat:bb4plv5x4rdbjnkrexc2chgi4a
Online Long-Term Trajectory Prediction Based on Mined Route Patterns
[chapter]
2020
Lecture Notes in Computer Science
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In this paper, we present a Big data framework for the prediction of streaming trajectory data by exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. ...
Subsequently, the trajectory prediction algorithm exploits these patterns in order to prolong the temporal horizon of useful predictions. ...
This research has also been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation ...
doi:10.1007/978-3-030-38081-6_4
fatcat:6n5heoumwfguln3chpcp35uifm
Probabilistic collision estimation system for autonomous vehicles
2016
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
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It is shown that in terms of robustness to noise the system successfully avoids collision. ...
This system could either be used as information and feedback for a trajectory planner or as a support for decision making at higher level system monitoring. ...
Long-term Trajectory Predictions The chosen approach for making long-term trajectory predictions in the system is based of the studies performed by [9] , [10] . ...
doi:10.1109/itsc.2016.7795597
dblp:conf/itsc/AnnellGS16
fatcat:n6cf6r77k5ckrd5zn2m5x2sxle
Approximate representations for multi-robot control policies that maximize mutual information
2014
Autonomous Robots
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We address the problem of controlling a small team of robots to estimate the location of a mobile target using non-linear range-only sensors. ...
using simulations and real world experiments in complex, indoor environments. ...
Figure 6b shows the long term error from a separate experiment where the target moves 15 m to the right of its starting location and then back. ...
doi:10.1007/s10514-014-9411-2
fatcat:5py5uqbdv5hu5h44ql6do7l7rm
Approximate Representations for Multi-Robot Control Policies that Maximize Mutual Information
2013
Robotics: Science and Systems IX
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We address the problem of controlling a small team of robots to estimate the location of a mobile target using non-linear range-only sensors. ...
using simulations and real world experiments in complex, indoor environments. ...
Figure 6b shows the long term error from a separate experiment where the target moves 15 m to the right of its starting location and then back. ...
doi:10.15607/rss.2013.ix.053
dblp:conf/rss/CharrowKM13
fatcat:d54mtgoiqveexix3gt4gds6nui
AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points
[article]
2020
arXiv
pre-print
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To better capture the moving objects in videos, we introduce dynamic points. ...
To the best of our knowledge, our method is the first to achieve unsupervised learning of trajectory extraction and prediction. ...
Finally, we use these trajectories to train the trajectory prediction network. The whole process uses no labels. ...
arXiv:2007.05719v1
fatcat:kdpkhkezqrgazjeg5mwicyowwa
Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
2015
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. ...
The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data. ...
Combining model predictive control with Gaussian process models has also been previously suggested in the control community (Kocijan et al. 2004) where it was used in a small trajectory tracking problem ...
doi:10.1609/aaai.v29i1.9623
fatcat:mkm5ui5ymvd7hcwaq2iw52yrsa
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