1,212,125 research outputs found
A realistic two-lane traffic model for highway traffic
A two-lane extension of a recently proposed cellular automaton model for
traffic flow is discussed. The analysis focuses on the reproduction of the lane
usage inversion and the density dependence of the number of lane changes. It is
shown that the single-lane dynamics can be extended to the two-lane case
without changing the basic properties of the model which are known to be in
good agreement with empirical single-vehicle data. Therefore it is possible to
reproduce various empirically observed two-lane phenomena, like the
synchronization of the lanes, without fine-tuning of the model parameters
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lane detection methods rely on a combination of highly-specialized,
hand-crafted features and heuristics, usually followed by post-processing
techniques, that are computationally expensive and prone to scalability due to
road scene variations. More recent approaches leverage deep learning models,
trained for pixel-wise lane segmentation, even when no markings are present in
the image due to their big receptive field. Despite their advantages, these
methods are limited to detecting a pre-defined, fixed number of lanes, e.g.
ego-lanes, and can not cope with lane changes. In this paper, we go beyond the
aforementioned limitations and propose to cast the lane detection problem as an
instance segmentation problem - in which each lane forms its own instance -
that can be trained end-to-end. To parametrize the segmented lane instances
before fitting the lane, we further propose to apply a learned perspective
transformation, conditioned on the image, in contrast to a fixed "bird's-eye
view" transformation. By doing so, we ensure a lane fitting which is robust
against road plane changes, unlike existing approaches that rely on a fixed,
pre-defined transformation. In summary, we propose a fast lane detection
algorithm, running at 50 fps, which can handle a variable number of lanes and
cope with lane changes. We verify our method on the tuSimple dataset and
achieve competitive results
Two-lane traffic rules for cellular automata: A systematic approach
Microscopic modeling of multi-lane traffic is usually done by applying
heuristic lane changing rules, and often with unsatisfying results. Recently, a
cellular automaton model for two-lane traffic was able to overcome some of
these problems and to produce a correct density inversion at densities somewhat
below the maximum flow density. In this paper, we summarize different
approaches to lane changing and their results, and propose a general scheme,
according to which realistic lane changing rules can be developed. We test this
scheme by applying it to several different lane changing rules, which, in spite
of their differences, generate similar and realistic results. We thus conclude
that, for producing realistic results, the logical structure of the lane
changing rules, as proposed here, is at least as important as the microscopic
details of the rules
Effects of Langmuir Kinetics of Two-Lane Totally Asymmetric Exclusion Processes in Protein Traffic
In this paper, we study a two-lane totally asymmetric simple exclusion
process (TASEP) coupled with random attachment and detachment of particles
(Langmuir kinetics) in both lanes under open boundary conditions. Our model can
describe the directed motion of molecular motors, attachment and detachment of
motors, and free inter-lane transition of motors between filaments. In this
paper, we focus on some finite-size effects of the system because normally the
sizes of most real systems are finite and small (e.g., size ). A
special finite-size effect of the two-lane system has been observed, which is
that the density wall moves left first and then move towards the right with the
increase of the lane-changing rate. We called it the jumping effect. We find
that increasing attachment and detachment rates will weaken the jumping effect.
We also confirmed that when the size of the two-lane system is large enough,
the jumping effect disappears, and the two-lane system has a similar density
profile to a single-lane TASEP coupled with Langmuir kinetics. Increasing
lane-changing rates has little effect on density and current after the density
reaches maximum. Also, lane-changing rate has no effect on density profiles of
a two-lane TASEP coupled with Langmuir kinetics at a large
attachment/detachment rate and/or a large system size. Mean-field approximation
is presented and it agrees with our Monte Carlo simulations.Comment: 15 pages, 8 figures. To be published in IJMP
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