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Alexander Greger och Fabian Burman, Elektroteknik
Titel: Flexible road modelling using a multi-object approach
Examinator: Lars Hammarstrand
Abstract:
Advanced
Driver Assistance Systems help drivers by increasing traffic safety and
comfort. Some of these systems, such as Adaptive Cruise Control, are
dependent on an accurate representation of the road in front of the
vehicle when making decisions. If the estimated geometry is inaccurate,
the systems' decisions will be based on a false representation of
reality, which can lead to accidents. By improving the road geometry
estimation utilized by such systems, the safety of all travelers on the
road can be increased.
A
problem present in many modern road estimation methods is that they are
inaccurate when modelling diverging or non-parallel lanes. This thesis
presents a set of flexible yet robust algorithms for estimating the host
lane and any diverging lanes. The problem is approached as a
multi-object tracking problem, and the thesis examines two different
multi-object tracking filters, the Gaussian Mixture Probability
Hypothesis Density (GM-PHD) filter and the Poisson Multi Bernoulli
Mixture (PMBM) filter. The PMBM is a robust tracker that performs well
in complex and easy scenarios, whereas the GM-PHD filter is a simpler
and more computationally efficient tracker that performs well on less
complex tracking scenarios.
The thesis results show that both
algorithms can model the host lane and diverging lanes on a highway. The
two filters have similar performance when modelling the host lane, but
the Poisson Multi Bernoulli Mixture is more proficient at detecting the
diverging lanes. However, a different evaluation method is required to
properly assess the geometry of the diverging lanes, since the current
method only evaluates the detection frequency.
Welcome!
Alexander, Fabian and Lars
Kategori
Studentarbete
Plats:
EDIT-rummet, meeting room, Hörsalsvägen 11, EDIT trappa C, D och H
Tid:
2022-06-07 15:00
Sluttid:
2022-06-07 16:00