3,404 research outputs found
Modeling rationality to control self-organization of crowds: An environmental approach
In this paper we propose a classification of crowd models in built
environments based on the assumed pedestrian ability to foresee the movements
of other walkers. At the same time, we introduce a new family of macroscopic
models, which make it possible to tune the degree of predictiveness (i.e.,
rationality) of the individuals. By means of these models we describe both the
natural behavior of pedestrians, i.e., their expected behavior according to
their real limited predictive ability, and a target behavior, i.e., a
particularly efficient behavior one would like them to assume (for, e.g.,
logistic or safety reasons). Then we tackle a challenging shape optimization
problem, which consists in controlling the environment in such a way that the
natural behavior is as close as possible to the target one, thereby inducing
pedestrians to behave more rationally than what they would naturally do. We
present numerical tests which elucidate the role of rational/predictive
abilities and show some promising results about the shape optimization problem
Two dimensional outflows for cellular automata with shuffle updates
In this paper, we explore the two-dimensional behavior of cellular automata
with shuffle updates. As a test case, we consider the evacuation of a square
room by pedestrians modeled by a cellular automaton model with a static floor
field. Shuffle updates are characterized by a variable associated to each
particle and called phase, that can be interpreted as the phase in the step
cycle in the frame of pedestrian flows. Here we also introduce a dynamics for
these phases, in order to modify the properties of the model. We investigate in
particular the crossover between low- and high-density regimes that occurs when
the density of pedestrians increases, the dependency of the outflow in the
strength of the floor field, and the shape of the queue in front of the exit.
Eventually we discuss the relevance of these results for pedestrians.Comment: 20 pages, 5 figures. v2: 16 pages, 5 figures; changed the title,
abstract and structure of the paper. v3: minor change
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
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous
vehicles must make responsible predictions in relation to surrounding
traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and
critical task is to explore the movement patterns of different traffic-agents
and predict their future trajectories accurately to help the autonomous vehicle
make reasonable navigation decision. To solve this problem, we propose a long
short-term memory-based (LSTM-based) realtime traffic prediction algorithm,
TrafficPredict. Our approach uses an instance layer to learn instances'
movements and interactions and has a category layer to learn the similarities
of instances belonging to the same type to refine the prediction. In order to
evaluate its performance, we collected trajectory datasets in a large city
consisting of varying conditions and traffic densities. The dataset includes
many challenging scenarios where vehicles, bicycles, and pedestrians move among
one another. We evaluate the performance of TrafficPredict on our new dataset
and highlight its higher accuracy for trajectory prediction by comparing with
prior prediction methods.Comment: Accepted by AAAI(Oral) 201
Pedestrian, Crowd, and Evacuation Dynamics
This contribution describes efforts to model the behavior of individual
pedestrians and their interactions in crowds, which generate certain kinds of
self-organized patterns of motion. Moreover, this article focusses on the
dynamics of crowds in panic or evacuation situations, methods to optimize
building designs for egress, and factors potentially causing the breakdown of
orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction
approach for pedestrians in environments cluttered with static obstacles. When
navigating in such workspaces shared with humans, robots need accurate motion
predictions of the surrounding pedestrians. Human navigation behavior is mostly
influenced by their surrounding pedestrians and by the static obstacles in
their vicinity. In this paper we introduce a new model based on Long-Short Term
Memory (LSTM) neural networks, which is able to learn human motion behavior
from demonstrated data. To the best of our knowledge, this is the first
approach using LSTMs, that incorporates both static obstacles and surrounding
pedestrians for trajectory forecasting. As part of the model, we introduce a
new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
space. We evaluate the benefit of interaction-aware motion prediction and the
added value of incorporating static obstacles on both simulation and real-world
datasets by comparing with state-of-the-art approaches. The results show, that
our new approach outperforms the other approaches while being very
computationally efficient and that taking into account static obstacles for
motion predictions significantly improves the prediction accuracy, especially
in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International
Conference on Robotics and Automation (ICRA) 201
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