10,213 research outputs found
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
Trajectory planning at high velocities and at the handling limits is a
challenging task. In order to cope with the requirements of a race scenario, we
propose a far-sighted two step, multi-layered graph-based trajectory planner,
capable to run with speeds up to 212~km/h. The planner is designed to generate
an action set of multiple drivable trajectories, allowing an adjacent behavior
planner to pick the most appropriate action for the global state in the scene.
This method serves objectives such as race line tracking, following, stopping,
overtaking and a velocity profile which enables a handling of the vehicle at
the limit of friction. Thereby, it provides a high update rate, a far planning
horizon and solutions to non-convex scenarios. The capabilities of the proposed
method are demonstrated in simulation and on a real race vehicle.Comment: Accepted at The 22nd IEEE International Conference on Intelligent
Transportation Systems, October 27 - 30, 201
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
Two-Stream RNN/CNN for Action Recognition in 3D Videos
The recognition of actions from video sequences has many applications in
health monitoring, assisted living, surveillance, and smart homes. Despite
advances in sensing, in particular related to 3D video, the methodologies to
process the data are still subject to research. We demonstrate superior results
by a system which combines recurrent neural networks with convolutional neural
networks in a voting approach. The gated-recurrent-unit-based neural networks
are particularly well-suited to distinguish actions based on long-term
information from optical tracking data; the 3D-CNNs focus more on detailed,
recent information from video data. The resulting features are merged in an SVM
which then classifies the movement. In this architecture, our method improves
recognition rates of state-of-the-art methods by 14% on standard data sets.Comment: Published in 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
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