11,495 research outputs found
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.Comment: 8 pages, 6 figure
Long-lived states in synchronized traffic flow. Empirical prompt and dynamical trap model
The present paper proposes a novel interpretation of the widely scattered
states (called synchronized traffic) stimulated by Kerner's hypotheses about
the existence of a multitude of metastable states in the fundamental diagram.
Using single vehicle data collected at the German highway A1, temporal velocity
patterns have been analyzed to show a collection of certain fragments with
approximately constant velocities and sharp jumps between them. The particular
velocity values in these fragments vary in a wide range. In contrast, the flow
rate is more or less constant because its fluctuations are mainly due to the
discreteness of traffic flow.
Subsequently, we develop a model for synchronized traffic that can explain
these characteristics. Following previous work (I.A.Lubashevsky, R.Mahnke,
Phys. Rev. E v. 62, p. 6082, 2000) the vehicle flow is specified by car
density, mean velocity, and additional order parameters and that are
due to the many-particle effects of the vehicle interaction. The parameter
describes the multilane correlations in the vehicle motion. Together with the
car density it determines directly the mean velocity. The parameter , in
contrast, controls the evolution of only. The model assumes that
fluctuates randomly around the value corresponding to the car configuration
optimal for lane changing. When it deviates from this value the lane change is
depressed for all cars forming a local cluster. Since exactly the overtaking
manoeuvres of these cars cause the order parameter to vary, the evolution
of the car arrangement becomes frozen for a certain time. In other words, the
evolution equations form certain dynamical traps responsible for the long-time
correlations in the synchronized mode.Comment: 16 pages, 10 figures, RevTeX
A Vehicular Traffic Flow Model Based on a Stochastic Acceleration Process
A new vehicular traffic flow model based on a stochastic jump process in
vehicle acceleration and braking is introduced. It is based on a master
equation for the single car probability density in space, velocity and
acceleration with an additional vehicular chaos assumption and is derived via a
Markovian ansatz for car pairs. This equation is analyzed using simple driver
interaction models in the spatial homogeneous case. Velocity distributions in
stochastic equilibrium, together with the car density dependence of their
moments, i.e. mean velocity and scattering and the fundamental diagram are
presented.Comment: 27 pages, 6 figure
New Insights into Traffic Dynamics: A Weighted Probabilistic Cellular Automaton Model
From the macroscopic viewpoint for describing the acceleration behavior of
drivers, this letter presents a weighted probabilistic cellular automaton model
(the WP model, for short) by introducing a kind of random acceleration
probabilistic distribution function. The fundamental diagrams, the
spatio-temporal pattern are analyzed in detail. It is shown that the presented
model leads to the results consistent with the empirical data rather well,
nonlinear velocity-density relationship exists in lower density region, and a
new kind of traffic phenomenon called neo-synchronized flow is resulted.
Furthermore, we give the criterion for distinguishing the high-speed and
low-speed neo-synchronized flows and clarify the mechanism of this kind of
traffic phenomena. In addition, the result that the time evolution of
distribution of headways is displayed as a normal distribution further
validates the reasonability of the neo-synchronized flow. These findings
suggest that the diversity and randomicity of drivers and vehicles has indeed
remarkable effect on traffic dynamics.Comment: 12 pages, 5 figures, submitted to Europhysics Letter
Bayesian Nonparametric Feature and Policy Learning for Decision-Making
Learning from demonstrations has gained increasing interest in the recent
past, enabling an agent to learn how to make decisions by observing an
experienced teacher. While many approaches have been proposed to solve this
problem, there is only little work that focuses on reasoning about the observed
behavior. We assume that, in many practical problems, an agent makes its
decision based on latent features, indicating a certain action. Therefore, we
propose a generative model for the states and actions. Inference reveals the
number of features, the features, and the policies, allowing us to learn and to
analyze the underlying structure of the observed behavior. Further, our
approach enables prediction of actions for new states. Simulations are used to
assess the performance of the algorithm based upon this model. Moreover, the
problem of learning a driver's behavior is investigated, demonstrating the
performance of the proposed model in a real-world scenario
Localized defects in a cellular automaton model for traffic flow with phase separation
We study the impact of a localized defect in a cellular automaton model for
traffic flow which exhibits metastable states and phase separation. The defect
is implemented by locally limiting the maximal possible flow through an
increase of the deceleration probability. Depending on the magnitude of the
defect three phases can be identified in the system. One of these phases shows
the characteristics of stop-and-go traffic which can not be found in the model
without lattice defect. Thus our results provide evidence that even in a model
with strong phase separation stop-and-go traffic can occur if local defects
exist. From a physical point of view the model describes the competition
between two mechanisms of phase separation.Comment: 14 pages, 7 figure
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