944 research outputs found
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Urban traffic scenarios often require a high degree of cooperation between
traffic participants to ensure safety and efficiency. Observing the behavior of
others, humans infer whether or not others are cooperating. This work aims to
extend the capabilities of automated vehicles, enabling them to cooperate
implicitly in heterogeneous environments. Continuous actions allow for
arbitrary trajectories and hence are applicable to a much wider class of
problems than existing cooperative approaches with discrete action spaces.
Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS)
in conjunction with Decoupled-UCT evaluates the action-values of each agent in
a cooperative and decentralized way, respecting the interdependence of actions
among traffic participants. The extension to continuous action spaces is
addressed by incorporating novel MCTS-specific enhancements for efficient
search space exploration. The proposed algorithm is evaluated under different
scenarios, showing that the algorithm is able to achieve effective cooperative
planning and generate solutions egocentric planning fails to identify
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
Efficient driving in urban traffic scenarios requires foresight. The
observation of other traffic participants and the inference of their possible
next actions depending on the own action is considered cooperative prediction
and planning. Humans are well equipped with the capability to predict the
actions of multiple interacting traffic participants and plan accordingly,
without the need to directly communicate with others. Prior work has shown that
it is possible to achieve effective cooperative planning without the need for
explicit communication. However, the search space for cooperative plans is so
large that most of the computational budget is spent on exploring the search
space in unpromising regions that are far away from the solution. To accelerate
the planning process, we combined learned heuristics with a cooperative
planning method to guide the search towards regions with promising actions,
yielding better solutions at lower computational costs
Ground states of dipolar gases in quasi-1D ring traps
We compute the ground state of dipoles in a quasi-one-dimensional ring trap
using few-body techniques combined with analytic arguments. The effective
interaction between two dipoles depends on their center-of-mass coordinate and
can be tuned by varying the angle between dipoles and the plane of the ring.
For weak enough interactions, the state resembles a weakly interacting Fermi
gas or an (inhomogeneous) Lieb-Liniger gas. A mapping between the Lieb-Liniger
and the dipolar-gas parameters in and beyond the Born approximation is
established, and we discuss the effect of inhomogeneities based on a
local-density approximation. For strongly repulsive interactions, the system
exhibits crystal-like localization of the particles. Their inhomogeneous
distribution may be understood in terms of a simple few-body model as well as a
local-density approximation. In the case of partially attractive interactions,
clustered states form for strong enough coupling, and the dependence of the
state on particle number and orientation angle of the dipoles is discussed
analytically.Comment: 15 pages, 10 figure
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a moving object or not. Compared to tracking approaches, that use
e.g. a particle filter to estimate grid cell velocities and then make a
decision for individual grid cells based on this estimate, our approach uses
the entire grid map as input image for a CNN that inspects a larger area around
each cell and thus takes the structural appearance in the grid map into account
to make a decision. Compared to our reference method, our concept yields a
performance increase from 83.9% to 97.2%. A runtime optimized version of our
approach yields similar improvements with an execution time of just 10
milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and
it was accapted at IV 201
Polarons and Molecules in a Two-Dimensional Fermi Gas
We study an impurity atom in a two-dimensional Fermi gas using variational
wave functions for (i) an impurity dressed by particle-hole excitations
(polaron) and (ii) a dimer consisting of the impurity and a majority atom. In
contrast to three dimensions, where similar calculations predict a sharp
transition to a dimer state with increasing interspecies attraction, we show
that the polaron ansatz always gives a lower energy. However, the exact
solution for a heavy impurity reveals that both a two-body bound state and
distortions of the Fermi sea are crucial. This reflects the importance of
particle-hole pairs in lower dimensions and makes simple variational
calculations unreliable. We show that the energy of an impurity gives important
information about its dressing cloud, for which both ans\"atze give inaccurate
results.Comment: 5 pages, 2 figures, minor change
Tunneling dynamics of few bosons in a double well
We study few-boson tunneling in a one-dimensional double well. As we pass
from weak interactions to the fermionization limit, the Rabi oscillations first
give way to highly delayed pair tunneling (for medium coupling), whereas for
very strong correlations multi-band Rabi oscillations emerge. All this is
explained on the basis of the exact few-body spectrum and without recourse to
the conventional two-mode approximation. Two-body correlations are found
essential to the understanding of the different tunnel mechanisms. The
investigation is complemented by discussing the effect of skewing the double
well, which offers the possibility to access specific tunnel resonancesComment: 10 pages, 8 figure
Fatal lymphoproliferation and acute monocytic leukemia-like disease following infectious mononucleosis in the elderly
Three elderly patients are reported, in whom serologically confirmed recent infectious mononucleosis is followed by fatal lymphoproliferation (case 1), by acute monocytic leukemia (case 2), and by acute probably monocytic leukemia (case 3)
Correlations in Ultracold Trapped Few-Boson Systems: Transition from Condensation to Fermionization
We study the correlation properties of the ground states of few ultracold
bosons, trapped in double wells of varying barrier height in one dimension.
Extending previous results on the signature of the transition from a
Bose-condensed state via fragmentation to the hard-core limit, we provide a
deeper understanding of that transition by relating it to the loss of coherence
in the one-body density matrix and to the emerging long-range tail in the
momentum spectrum. These are accounted for in detail by discussing the natural
orbitals and their occupations. Our discussion is complemented by an analysis
of the two-body correlation function.Comment: 22 pages, 7 figure
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