15,453 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Long-term situation prediction plays a crucial role in the development of
intelligent vehicles. A major challenge still to overcome is the prediction of
complex downtown scenarios with multiple road users, e.g., pedestrians, bikes,
and motor vehicles, interacting with each other. This contribution tackles this
challenge by combining a Bayesian filtering technique for environment
representation, and machine learning as long-term predictor. More specifically,
a dynamic occupancy grid map is utilized as input to a deep convolutional
neural network. This yields the advantage of using spatially distributed
velocity estimates from a single time step for prediction, rather than a raw
data sequence, alleviating common problems dealing with input time series of
multiple sensors. Furthermore, convolutional neural networks have the inherent
characteristic of using context information, enabling the implicit modeling of
road user interaction. Pixel-wise balancing is applied in the loss function
counteracting the extreme imbalance between static and dynamic cells. One of
the major advantages is the unsupervised learning character due to fully
automatic label generation. The presented algorithm is trained and evaluated on
multiple hours of recorded sensor data and compared to Monte-Carlo simulation
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
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