# Fluorescence of pyrene-doped polystyrene films from room temperature down to
4 K for wavelength-shifting applications

H. Benmansour  E. Ellingwood  Q. Hars  P.C.F. Di Stefano11footnotetext:
Corresponding author. D. Gallacher  M. Kuźniak  V. Pereimak  J. Anstey  M.G.
Boulay  B. Cai  S. Garg  A. Kemp  J. Mason  P. Skensved  V. Strickland  M.
Stringer

###### Abstract

In liquid argon-based particle detectors, slow wavelength shifters (WLSs)
could be used alongside the common, nanosecond scale, WLS tetraphenyl
butadiene (TPB) for background mitigation purposes. At room temperature,
pyrene has a moderate fluorescence light yield (LY) and a time constant of the
order of hundreds of nanoseconds. In this work, four pyrene-doped polystyrene
films with various purities and concentrations were characterized in terms of
LY and decay time constants in a range of temperature between 4 K and 300 K
under ultraviolet excitation. These films were found to have a LY between 35
and 50% of that of evaporated TPB. All light yields increase when cooling
down, while the decays slow down. At room temperature, we observed that pyrene
purity is strongly correlated with emission lifetime: highest obtainable
purity samples were dominated by decays with emission time constants of $\sim$
250–280 ns, and lower purity samples were dominated by an $\sim$ 80 ns
component. One sample was investigated further to better understand the
monomer and excimer emissions of pyrene. The excimer-over-monomer intensity
ratio decreases when the temperature goes down, with the monomer emission
dominating below $\sim 87$ K.

##  1 Introduction

Noble liquids are used as particle detection mediums due to their high
scintillation light yields, excellent radio purity capabilities and particle
identification abilities. Experiments using noble liquid detectors include
direct dark matter detection [1, 2, 3, 4, 5, 6], neutrinoless double-beta
decay [7], and neutrino observation [8]. In such detectors, a particle
interaction in the liquid produces scintillation light roughly proportional to
the energy deposited. The vacuum ultra-violet (UV) scintillation emission of
liquid argon and xenon ($\sim 128$ nm and $\sim 175$ nm respectively) make
light detection difficult with standard detectors such as photomultipliers
(PMs) [9].

One common way to deal with this is to use a wavelength shifter (WLS), a
material which absorbs the UV light and re-emits it as easier-to-detect
visible light. A standard WLS is 1,1,4,4-tetraphenyl-1,3-butadiene (TPB),
characterized by a short, $\sim$ ns, time constant [10], and high conversion
efficiency [11]. In certain cases, for instance to identify different regions
of the detector [12], it may be advantageous for the WLS to have significantly
longer time constants than TPB and argon, enabling a form of spatial pulse-
shape discrimination. A similar approach can also be used in liquid
scintillator detectors for separation of Cherenkov and scintillation
components [13] or other purposes [14].

In this work, we study the fluorescence light yield and decay profiles of four
pyrene-doped polystyrene (PS) coatings with diverse pyrene purities and
concentrations, excited by a 285 nm LED at temperatures between 4 K and 300 K,
including the 87 K boiling point of liquid argon. Fluorescence yield and decay
profiles were both determined from time-resolved measurements. One sample was
investigated further with spectral measurements to characterize the monomer
and excimer responses.

##  2 Wavelength Shifters, Excimers and Monomers

In particle detectors using noble liquids such as argon, a WLS coating is
generally applied between the liquid and the light detectors. WLS are
materials that absorb the UV light and re-emit it by fluorescence in the
visible spectrum. As noble elements are liquids at low temperatures (87 K for
argon), it is important to characterize the WLS in such conditions.

Pyrene (C16H10) is a polycyclic aromatic hydrocarbon consisting of four fused
benzene rings. Two adjacent molecules of pyrene (monomers) can bond together
forming a dimer with an interplanar distance of 3.5 Å [15]. Both monomers and
dimers can be excited; the latter are called excimers. As a pure crystal,
pyrene has a high vapor pressure of $6\times 10^{-6}$ mbar [16] making it
unsuitable for the vacuum requirements of many wavelength-shifting setups. It
is therefore often used as a dopant in a more stable matrix of a material like
poly(methyl methacrylate) (PMMA, a.k.a. acrylic) [17, 18] or polystyrene (PS)
[18]. Room-temperature fluorescence spectra of pyrene in PS films with
different pyrene concentrations exhibit two distinct, but broad, emission
features, from the monomer ($\sim$ 395 nm), and excimer states ($\sim$ 470
nm), of pyrene [19]. The amount of excimer emission increases with pyrene
concentration [19]. At room temperature, fluorescence decay of diluted monomer
happens on a scale of $\sim$ 200 ns [19] — of interest here, since it is
significantly slower than TPB and the singlet lifetime of liquid argon.

Fluorescence of pyrene in solid matrices involves three mechanisms [20].
Monomer emission occurs when an isolated pyrene molecule absorbs light, then
de-excites radiatively to the ground state. Dynamic excimer emission takes
place when an exciton migrates from an excited monomer to a preassociated
dimer [21], creating an excimer which de-excites radiatively. Formation of
dynamic excimer typically takes place on timescales of nanoseconds or more.
Static excimer emission happens when a preassociated dimer is excited, then
de-excites radiatively. Due to their proximity, the molecules in the dimer
have perturbed absorption and excitation spectra. Static formation takes place
on picosecond timescales. Conversion can occur between the processes described
above. Mechanisms of monomer and excimer fluorescence are summarized in Fig.
1. While the probabilities of radiative decay do not depend on temperature,
temperature dependent non-radiative decays can also take place. The
probability of these tends to decrease as temperature falls, contributing to
slower decays and generally higher light yields [22].

Figure 1: Pyrene fluorescence diagram. M is a ground state pyrene, M* is a
pyrene monomer in the first excited state. E* is the dynamic excimer formed by
(M*+M), D is the associative ground state pyrene dimer and D* is the dimer in
the first excited singlet state. Grey arrows relate to the pyrene molecule
processes and blue relate to the fluorescence. Adapted from [20].

##  3 Equipment and Setup Details

The pyrene fluorescence studies involve data from two different setups. Both
feature acrylic substrates coated with pyrene-polystyrene films installed in
an optical cryostat [23] to make measurements at temperatures from 300 K down
to 4 K under UV excitation. Measurements were taken sequentially while cooling
to avoid thermoluminescence, with cross-check measurements performed while
warming. Cryostat pressure was $\lesssim 10^{-6}$ mbar.

The time-resolved measurements of the pyrene sample are used to study the
fluorescence light yield (number of photons emitted by fluorescence for a
given excitation) and decay time of the pyrene coating. This setup features a
fast pulsing LED exciting the sample and a PMT to collect the pyrene
fluorescence light. The spectral measurement uses the same LED, but with a
constant brightness, to excite the sample and a spectrometer to analyze the
pyrene fluorescence spectrum.

Figure 2: Left: Setup for time-resolved measurements. 285 nm LED light
interacts with the pyrene and the acrylic from the sample. The resulting
fluorescent light from the sample passes through a filters to select all or
parts of the spectrum before being detected on the PMT. Adapted from [24].
Right: Image of an acrylic sample attached to the cold finger of the optical
cryostat. The cryostat thermometer is visible on the left side of the cold
finger.

###  3.1 Samples

The samples used in this study were made at Carleton University and consist of
a pyrene polystyrene mix applied as a film on Reynolds Polymer Technology
(RPT) acrylic substrates that were machined to fit in the optical cryostat as
shown in Fig. 2. Details of pyrene coating preparation can be found elsewhere
[12]. Information on the RPT acrylic can be found in [24].

A total of four different pyrene-polystyrene mixes are studied in this paper.
There are three samples with a 15% pyrene concentration by weight (equivalent
to $7.78\times 10^{-4}$ mol/$\mbox{cm}^{3}$) but three different fluorescence
grades (98%, 99% and 99.9% purity), called P1598, P1599 and P15 respectively.
There is one sample with a 12% pyrene concentration in weight (equivalent to
$6.22\times 10^{-4}$ mol/$\mbox{cm}^{3}$) and a 99.9% fluorescence grade
called P12. The pyrene-polystyrene coating was applied on the acrylic
substrate with a syringe for P12 and P15, and with a nylon bristled brush for
P1598 and P1599.

This report will compare the results from these four samples. An identical
substrate coated with evaporated TPB provides a fifth sample for reference.
The full analysis of TPB will be presented elsewhere [25].

###  3.2 Time-Resolved Mode

The setup used for the time-resolved measurements in presented in Fig. 2. The
excitation is provided by a 285 nm LED. A function generator produces a 2 V
amplitude 50 Hz square wave. This signal is fed into the pulser system which
consists of a Kapunstinsky fast-timing pulser circuit [26] that powers the UV
LED. This system produces a 285 nm pulse which has a FWHM of $\sim 6$ ns.
Between the LED and the entry window of the cryostat there is a TECHSPEC Hard
Coated OD 4.0 10 nm bandpass filter with a central wavelength at 280 nm and a
10 nm FWHM. This filter ensures that the sample is only excited by the
wavelength of interest from the LED.

The luminescence of the pulser system can be dependent on the ambient
temperature of the room. A W1209 temperature controller was used to stabilize
the temperature of this system by heating it with a resistor attached to the
outside of the metal box housing the LED and the Kapustinsky circuit. This
kept the circuit and LED at (29.0$\pm$0.1)∘C.

The 285 nm LED pulses interact with the pyrene coating producing a distinct
fluorescence spectrum with a light yield and pulse decay timing that should
depend on the temperature of the sample inside the cryostat. The light then
goes through the acrylic substrate, which cuts off any remaining UV component.
The optical ordering of UV light, pyrene-doped coating, acrylic, light
detection, is similar to that of an argon particle detector like DEAP, made of
acrylic, with the inner surface coated with pyrene-doped polystyrene and the
light detectors on the other side [12].

The light emitted by the sample passes through the exit window of the
cryochamber and then goes through a 660SP Rapid Edge bandpass filter from
Omega Optical which accepts light between 375 nm and 660 nm in order to reject
any remaining 285 nm LED light. A motorized filter wheel was installed between
the UV rejection filter and the PMT to switch between filters that could
separate the monomer and excimer components of the pyrene fluorescence
emission. The excimer is studied using a Schott GG455 longpass filter with a
cut-on wavelength of 455 nm. The monomer part of the spectrum is studied using
a Hoya U330 bandpass filter with a 330 nm central wavelength and a 140 nm
FWHM. The transmission spectrum for these two filters and the broad bandpass
spectrum are shown in Fig. 3.

Figure 3: Left: Filters used in time resolved measurements. 660SP Rapid Edge
bandpass filter between the cryostat exit window and the PMT, U330 bandpass
filter for monomer measurements, GG455 filter for excimer. The P15 spectrum at
87 K with only the PMT bandpass applied is also shown to demonstrate where
these filters cut on the spectrum. Right: TPB spectra at multiple temperatures
[24], compared to the PMT quantum efficiency, as a function of wavelength
[27].

A Hamamatsu R6095-100 photomultiplier tube (PMT) with a super-bialkali
photocathode and borosilicate glass is used to collect the fluorescence light
from the pyrene which is powered by a CAEN high voltage power supply and
recorded using a digitizer. This study uses a National Instruments PXIe-5160
digitizer which has a 10-bit analog input resolution with a 2.5 GS/s sampling
rate. The 50 Hz function generator pulse that is fed into the Kapustinsky
circuit is also used as the trigger for the digitizer while the PMT output is
fed into one of the digitizer channels. The PMT pulse is recorded for 12
$\mu$s sampled every 0.4 ns. The first 1.2 $\mu$s of the record are
pretrigger. A run at a specified temperature consists of 45 000 of these 12
$\mu$s events.

###  3.3 Spectral Mode

The basic setup of this experiment is similar to the time-resolved
measurements in its use of the 285 nm LED and the cryostat. The spectra were
taken at seven temperatures: 300 K, 250 K, 163 K, 87 K, 77 K, 27 K and 4 K.
The two main differences between the time-resolved and spectrometer setup were
the LED mode and that the PMT was replaced with a spectrometer. The 285 nm LED
was run in DC mode to provide a continuous light source as opposed to a pulsed
source. To do this the Kapustinsky circuit was bypassed and the LED was
provided with a continuous power input.

This experiment uses a Horiba microHR spectrometer with a Horiba Symphony II
liquid-nitrogen cooled open electrode 2D CCD array. The spectrometer and the
cryostat are connected via an optical fiber at the same position on the
cryostat as the PMT was for the time-resolved measurements. The spectrometer
settings, including the filter, wavelength range and exposure, and data
acquisition are controlled by the SynerJY program on the DAQ computer. The
spectrum window was from 350–650 nm and the exposure was set to 10 s. For each
spectrum, an additional exposure with the system shielded from light was
subtracted to remove dark counts.

##  4 Analysis Techniques

###  4.1 Data Reduction

The digitizer is triggered and synchronized with the LED pulser so there are
no spurious pulses in the data set and no cuts have been applied to the data.
The data are reduced by two different methods:

  * •

An average pulse is calculated for all the events in a run; the baseline is
determined from the pretrigger and subtracted from the average.

  * •

Reduced quantities are calculated for individual pulses; they include the
baseline and its sample-by-sample standard deviation, and pulse integral
between user-specified bounds.

Data have been analyzed and plotted using the NumPy [28] and Matplotlib [29]
python libraries.

###  4.2 LED-Induced Noise

In all time-resolved measurements, an electronic noise oscillation around the
baseline was observed which is due to the pulser circuit. To better understand
its characteristics and mitigate its effects, there were dedicated noise runs
at at least one temperature, usually 300 K, for each study of a sample. The
noise run was taken with an identical procedure to the other time-resolved
measurements except that the shutter between the LED and cryochamber was
closed. It was then possible to observe noise-only events without the light
emitted by the sample.

Figure 4: For the analysis, the average noise (red) is removed from the raw
average pulse (blue). The average pulse (black) obtained is used to do the
fits.

The red curve in Fig. 4 (top) is an average of the 45 000 noise events. We
notice that there is a consistent pattern of fast oscillations with a
frequency of several hundreds MHz. This consistency, both in shape and timing,
allows us to mitigate the noise by simply subtracting the average pulse shape
of a noise-only run from the average shape of any run. The noise and its
fluctuations are accounted for in the $\chi^{2}$ calculation used for pulse
fitting as explained in Sec. 4.5.1.

###  4.3 SPE Analysis

This analysis determines the number of detected photoelectrons as a proxy for
light yield. For this, the average single photoelectron (SPE) integral has to
be estimated. SPE integral distributions are obtained by reducing the LED
voltage to a level where the PMT detects one photoelectron or less for each
event. The integration time for each pulse is set to 60 ns. The distribution
of these integrals was then fitted with a model describing the photomultiplier
response [30], as shown in Fig. 5. This SPE integral distribution corresponds
to an empty cryostat configuration, i.e. to the LED barely illuminating the
PMT with no sample in between. The reference SPE integral value corresponds to
the average integral of a single photoelectron. This property is
characteristic of the PMT and high voltage used, and also depends on the
vertical range used on the DAQ window.

Figure 5: Data obtained with no sample in an empty cryostat. Left: Fitted
single photoelectron charge distribution. Average number of photoelectrons per
LED pulse is $m=0.11$; average SPE integral is $13.83\pm 0.04\mbox{
ADU}\cdot\mu\mbox{s}$. Right: Normalized shape of LED pulse and PM response.

For this PMT, several SPE runs were carried out with pyrene samples at
different temperatures in addition to the empty-cryostat run in Fig. 5. The
average SPE integral value was found to be equal to $(13.80\pm
0.04)~{}\mbox{ADU}\cdot\mu\mbox{s}$, using a vertical range
$\pm~{}0.25~{}\mbox{V}$ for the DAQ. The integral of a given pulse can then be
converted into a number of photoelectrons dividing by this SPE integral value.

Lastly, Fig. 5 illustrates the average shape of the LED pulse and PMT
response, obtained in the empty cryostat, but with a higher LED intensity to
minimize the electronic noise. After normalization, this is used as the
instrumental component during pulse deconvolution (Sec. 4.5). Taking this
pulse as a proxy for the noisier actual SPE pulses, the shape justifies the 60
ns integration window.

###  4.4 Fluorescence Yield

To estimate the light yield in each set of conditions, we calculate the
integral of each event trace in a run by summing the amplitudes in a given
window and weighting by the sampling time. The result is expressed as a number
of detected photoelectrons, a proxy for the light yield since the excitation
is the same in all runs. A 3 $\mu\mbox{s}$ window is chosen containing more
than 99.9% of the overall amount of light, as shown in Fig. 6. This figure
also shows the distribution of integrals for all 45 000 events in a run for a
pyrene sample at different temperatures.

Figure 6: Left: an event from a pyrene run with two red vertical lines (at
1270 ns and 4270 ns) representing the lower and upper limits of the
integration window. Individual photoelectrons are clearly visible. Right:
event integral distribution for three runs at different temperatures. The blue
distribution (300 K) is fitted with a skew normal distribution as an example.
The mean of this distribution provided light yield for the sample at that
temperature.

The average detected light yield is then obtained by fitting these
distributions with a skew normal function [31]. A fit example is shown in Fig.
6. It is carried out by minimizing a Baker-Cousins likelihood [32]. The mean
is represented by a vertical dashed red line. The fit of each distribution
allows us to calculate the average detected fluorescence yield of the pyrene
sample at each temperature. Comparison between temperatures tells us how the
absolute light yield changes with temperature. Using the same method for the
TPB sample, we can access the relative light yield of the sample.

###  4.5 Fluorescence Profile Fitting

One objective of this report is to explore the fluorescence decay times of the
four pyrene samples at different temperatures. This requires fitting the
average pulse shape with the right physically motivated mathematical model.
Fits were carried out in a $[1285~{}\mbox{ns}-5000~{}\mbox{ns}]$ window
containing at least 99.9% of the light detected in the event. Fits of the data
($d_{i}$ photoelectrons at sample $i$) were carried with a model consisting of
the electronic noise ($z_{i}$ photoelectrons at sample $i$, determined with
the shutter closed as in Sec. 4.2) and the convolved ideal pulse shape
($\nu_{i}$ photoelectrons at sample $i$) as described in the next paragraph.
Statistical fluctuations of the number of photoelectrons (standard deviation
$\sigma_{i}$) dominate the electronic fluctuations ($\sigma^{\prime}_{i}$) as
seen in Fig. 6. Fits are carried out by minimizing
$\chi^{2}=\Sigma_{i}\frac{(d_{i}-z_{i}-\nu_{i})^{2}}{\sigma_{i}^{2}+\sigma^{{}^{\prime}2}_{i}}$.

The fluorescence profiles $I(t)$ (in photoelectrons/second) can be modelled by
the convolution of the excitation pulse $E(t)$ (in $s^{-1}$) and the true
decay model $i(t)$ (in photoelectrons/second): $I(t)=E(t)\ast i(t)$. Before
fitting, the noise is subtracted from the pyrene average trace in accordance
with Sec. 4.2. The excitation pulse $E(t)$ is obtained by having the LED
shining on the PMT directly. The pulse obtained then undergoes noise
subtraction and is normalized to an integral of 1, see Fig. 5.

####  4.5.1 Fits to Full Pulseshape

The whole pyrene fluorescence profiles for all four samples were fitted using
a sum of three exponentials: two decays and one rise. This ad-hoc model allows
us to compare the time constants involved in the various pyrene fluorescence
profiles of each sample. The model is presented in Eq. 4.1 with $N_{k}\geq 0$
and $\tau_{k}\geq 0$:

$i(t)=-\frac{N_{rise}}{\tau_{rise}}e^{-\tfrac{t}{\tau_{rise}}}+\frac{N_{1}}{\tau_{1}}e^{-\tfrac{t}{\tau_{1}}}+\frac{N_{2}}{\tau_{2}}e^{-\tfrac{t}{\tau_{2}}}$
(4.1)

A fit using this model on the whole fluorescence profile of P15 at 87 K is
shown in Fig. 7. Errors of $\pm 5\%$ were attributed to the values obtained
from the fit to account for the systematic and statistical errors from the
fit.

Figure 7: Left: Fit of P15 fluorescence profile at 87 K using Eq. 4.1. (zoom
on main feature of pulse and full window in log scale). Right: Pulse
reconstruction from monomer and excimer profiles at 87 K. The monomer (green)
and excimer (blue) contributions are scaled so that the sum (yellow) is the
closest fit to the total pulse data (black).

The contribution to the fluorescence light from the different components can
be calculated by integrating the model: $N_{tot}=\int
i(t)dt=N_{1}+N_{2}-N_{rise}$. The fractional contribution of each of the three
components is :

$F_{rise}=\frac{N_{rise}}{N_{tot}}~{}~{}~{};~{}~{}\\\
F_{1}=\frac{N_{1}}{N_{tot}}~{}~{}~{};~{}~{}\\\ F_{2}=\frac{N_{2}}{N_{tot}}.$
(4.2)

####  4.5.2 Monomer and Excimer Separation

The pyrene fluorescence is made of two distinct emissions: monomer and
excimer. We have observed them separately using a 330 nm bandpass filter and a
455 nm longpass filter (more details in Sec. 3). A linear combination of them
matches the full pulse shape, as shown in Fig. 7 for 87 K. This reconstruction
allows us to determine the proportion of monomer and excimer in the total
light emitted by the sample at each temperature. To find out the fluorescence
decay times, we separately fit the monomer-only and the excimer-only
fluorescence profiles.

The unconvoluted monomer profile for pyrene in PS can be described as an
exponential decay accelerated by excimer formation [19]:

$i_{m}(t)=\frac{N_{1}^{\prime}}{\tau_{1}^{\prime}}e^{-\tfrac{t}{\tau_{1}^{\prime}}-2q\sqrt{\tfrac{t}{\tau_{1}^{\prime}}}}$
(4.3)

The first parameter is a decay time $\tau_{1}^{\prime}$ corresponding to a
pure exponential decay in a dilute monomer system with no excimer formation.
The second parameter is dimensionless quantity $q$, the ratio of the dimer
concentration to a critical dimer concentration, itself defined as the
concentration at which the rate transfer from excited monomer to excimer is
equal to the total rate of excited monomer decay by all other channels. This
parameter characterizes the non-exponential nature of the decay caused by
excimer formation. Lastly, the total number of photons emitted is
$N_{1}^{\prime}\left(1-qe^{q^{2}}\sqrt{\pi}\left[1-{\mbox{erf}}(q)\right]\right)$
(Appendix A). This model is used to fit the monomer decay in Fig. 8. The decay
models for the monomer and excimer are also shown.

Figure 8:  (a): Monomer fit to 87 K data on the full fit window in log scale
using the model presented in Eq. 4.3. (b): Zoom on fit. (c): and monomer decay
model. Bottom three figures (d), (e) and (f) are the same, but for excimer.
Excimer decay model shows a non-zero initial value corresponding to static
excimer, and a subsequent rise, corresponding to dynamic excimer.

For the excimer-only fluorescence, the decay cannot be described by one single
exponential; instead we use two decay constants $\tau_{2}^{\prime}$ and
$\tau_{3}^{\prime}$. In addition, excimer models introduce two populations of
excimer: dynamic and static (Sec. 2). A rise time ($\tau_{rise}^{\prime}$) is
included in the model to account for the formation of the dynamic excimer. The
model is:

$i_{e}(t)=-\frac{N_{rise}^{\prime}}{\tau_{rise}^{\prime}}e^{-\tfrac{t}{\tau_{rise}^{\prime}}}+\frac{N_{2}^{\prime}}{\tau_{2}^{\prime}}e^{-\tfrac{t}{\tau_{2}^{\prime}}}+\frac{N_{3}^{\prime}}{\tau_{3}^{\prime}}e^{-\tfrac{t}{\tau_{3}^{\prime}}}$
(4.4)

The fit shows that at $t=0$, $i_{e}\neq 0$, supporting the existence of
instantaneous excimer formation, static excimer. The slower rise in the first
20 ns of the pulse indicates the existence of a delayed excimer formation,
corresponding to the dynamic excimer.

The contribution to the fluorescence light from the different components can
be calculated by integrating the fitting model over the fitting window
(Appendix A):

$F_{monomer}=\frac{N_{m}^{\prime}}{N_{tot}^{\prime}}=\frac{N_{1}^{\prime}}{N_{tot}^{\prime}}\left(1-qe^{q^{2}}\sqrt{\pi}\left(1-\mbox{erf}(q)\right)\right)$
(4.5)

$F_{excimer}=\frac{-N_{rise}^{\prime}+N_{2}^{\prime}+N_{3}^{\prime}}{N_{tot}^{\prime}},$
(4.6)

with
$N_{tot}^{\prime}=N_{m}^{\prime}+N_{2}^{\prime}+N_{3}^{\prime}-N_{rise}^{\prime}$.

###  4.6 Spectra

For the spectrometer measurements, the main spectrum was recorded between 350
nm and 650 nm, with 0.3 nm bins. The evolution of the monomer and excimer
intensities with temperatures are calculated. No correction was applied to the
spectral measurements and the analysis obtained from them. The results are
shown in Section 5.1.

##  5 Results

###  5.1 Temperature Dependent Emission Spectra

We performed detailed spectral measurements of one sample, P15, to study the
contributions of monomer and excimer and their temperature-dependent changes.
The spectrum also helps to pick suitable filters to study the monomer and
excimer components separately for the time-resolved measurements.

Figure 9: Spectra of P15 at various temperatures. The main monomer spectrum
corresponds to the series of peaks below $\sim 435$ nm while the excimer
corresponds to the broad distribution above about $\sim 435$ nm. The UV-
absorbing acrylic substrate explains the sharp drop below 390 nm.

Fig. 9 shows the raw spectra measured using the cryostat and spectrometer for
different sample temperatures. The spectra decrease sharply at wavelengths
below 390 nm because of absorption from the samples’ acrylic substrate. Based
on these spectra, the filters to use in the time resolved measurements were
chosen to maximize the light yield from the monomer (330 nm bandpass filter)
and excimer components (455 nm longpass filter) in wavelength regions where
one of the components dominates. Integrated monomer and excimer contributions
are shown in Fig. 10. As the temperature decreases, the monomer contribution
increases, while the excimer contribution increases at first and then
decreases for temperatures below $\sim$ 150 K.

Figure 10: Monomer (left) and excimer (right) intensity obtained from spectrum
integration as a function of P15 temperature. Monomer is defined as the region
of the spectrum with wavelengths lower than 435 nm and excimer as the region
with higher wavelengths. The intensity of the monomer increases with
decreasing temperature, while for the excimer, it increases at first with
decreasing temperature and then starts to decrease.

The filters chosen for the time-resolved measurement monomer and excimer study
were based on the measured pyrene fluorescence spectrum. The filter
transmission spectra are shown in Fig. 3. The 660SP bandpass before the sample
is always present during the time-resolved measurements to limit any stray UV
LED light from reaching the PMT. To study the whole time-resolved pulse only
this filter is present. The monomer is studied using a combination of the U330
bandpass, shown in orange, and the 660SP bandpass. Although the U330 filter
cuts out the majority of the peak, the goal was to ensure that the wavelength
range limited the broad excimer peak contribution. The excimer is studied
using a combination of the GG455 longpass and the 660SP bandpass filters.

###  5.2 Time-Resolved Fluorescence Yield

The fluorescence yields of four pyrene samples with varying concentrations and
purities were studied: P12 (99.9% purity and 12% concentration), P15 (99.9%
purity and 15% concentration), P1599 (99% purity and 15% concentration) and
P1598 (98% purity and 15% concentration). Using the fits of integral
distributions presented in Fig. 6, the number of photoelectrons detected from
each pyrene sample is calculated and compared to the number of photoelectrons
detected from TPB [25] to provide a proxy of the relative light yield. The
result is shown in Fig. 11. The amount of light emitted by all pyrene samples
compared to TPB is relatively stable and is between 35% for P1599 at 300 K and
46% for P15 at 4 K. Going from 300 K to 87 K, the relative light yield for P15
increases by 10%.

Figure 11: Left: fluorescence yield of the TPB sample (top) and P15, P12,
P1599 and P1598 (bottom) expressed as a number of photoelectrons at different
temperatures. Results from cross-check (cc) runs at 87 K and room temperature
are also shown. Right: fluorescence yield of the pyrene samples relatively to
TPB at different temperatures. Quantum efficiency-corrected P15 relative light
yield is also displayed.

In the case of sample P15, we can use the monomer and excimer data to correct
the light yield for PMT quantum efficiency. The result is overlayed on Fig.
11. This correction increases the relative light yield of P15 with respect to
TPB (eg 60% vs 41% at 300 K). The corrected relative light yield falls by 10%
when the temperature decreases from 300 K to 87 K.

###  5.3 Fluorescence Decay

The fluorescence profiles of the four pyrene samples were fitted using the
model from Eq. 4.1 in accordance with the method described in Sec. 4.5.1. Fig.
12 gives the values for the two decay time constants $\tau_{1}$ and $\tau_{2}$
for temperatures ranging between 4 K and 300 K for four different pyrene
samples: P12 (99.9% purity and 12% concentration), P15 (99.9% purity and 15%
concentration), P1599 (99% purity and 15% concentration) and P1598 (98% purity
and 15% concentration). The contributions $F_{i}$ from $\tau_{i}$ are defined
in Eq. 4.2 (note that $-F_{rise}+F_{1}+F_{2}=1$ so $F_{1}+F_{2}\geq 1$).
Because $F_{rise}\leq 4\%$ for all samples at all temperatures, the
contribution from the rise component is negligible compared to the other
contributions. This contribution is therefore considered as part of the
uncertainty on the other contributions.

Figure 12: Left: time constants $\tau_{1}$ and $\tau_{2}$ for the four
different pyrene samples. Right: contribution of each time constant to the
light yield.

From these plots it is clear that the concentration and the purity of pyrene
seem to have little influence on $\tau_{1}$ values at higher temperatures. At
lower temperatures, the highest purity samples, P12 and P15, show higher
values of $\tau_{1}$. The discrepancy is clearer when looking at the longer
time constant, $\tau_{2}$. Looking at two samples with similar pyrene purities
but different pyrene concentrations (P15 and P12), it can be seen that the
highest concentration leads to a longer time constant. Looking at three
samples with similar pyrene concentrations but different pyrene purities (P15,
P1599 and P1598) shows that the highest purity leads to a longer time
constant. On the one hand, by comparing P15, P1599 and P1598, it is clear that
the lower pyrene purity leads to a much higher contribution from the short
time constant $\tau_{1}$ (70% vs 30% at 300 K). On the other hand, a decreased
pyrene concentration leads to a higher contribution from the long time
constant $\tau_{2}$ (80% vs 70% at 300 K).

###  5.4 Fluorescence Decay Times for Excimer and Monomer Emissions of the P15
Sample

The decay profiles are fitted using the models from Eq. 4.3 and Eq. 4.4 for
the monomer and the excimer profiles respectively. Examples of fit are shown
in Fig. 8. Fig. 13 plots the evolution of the parameter $q$ from the monomer
model and of the three fluorescence decay times: one corresponding to the
monomer emission, one to the short excimer emission, and one to the long
excimer emission. For each component, the contribution to the total light can
be determined as a portion of the total light emitted by the sample (Eq. 4.2).
As in the previous section, because $F^{\prime}_{rise}\leq 4\%$ for all
samples at all temperatures, the contribution from the rise component is
negligible compared to the other contributions. This contribution is therefore
considered as part of the uncertainty on the other contributions. The method
is described in Sec. 4.5.2.

Figure 13: Left: Fluorescence decay times for P15. The monomer profiles are
fitted with the model from Eq. 4.3 and the excimer profiles are fitted with
model from Eq. 4.4. Parameter $q$ from the model in Eq. 4.3 is also shown.
Right: Contribution to the total light from each component of P15 fluorescence
decay. Total excimer represents the sum of the short excimer and the long
excimer contribution.

We notice that $\tau_{2}^{\prime}$ is the shortest decay time with values
around 100 ns. $\tau_{2}^{\prime}$ does not seem to be particularly dependent
on the sample temperature. The other decay times all have an increasing trend
with decreasing temperature. The evolution of parameter $q$ from Eq. 4.3 is
also displayed on Fig. 13. This parameter from the monomer decay model
correlates to monomer conversion into excimers [19].

At room temperature, the majority of the light emitted by the sample is due to
the excimer emission, but it is equally distributed between the three decay
components. When the temperature decreases, the monomer increases its share of
the total light while excimer light decreases. At 87 K, the monomer emission
represents almost 70% of the the total light. The decay is therefore dominated
by a 280 ns component.

As discussed in Sec. 5.2, the PMT QE differs between the monomer and the
excimer emission bands. The contribution to the light presented in Fig. 13 can
therefore be corrected. The QE-corrected contributions are shown in Fig. 14.
As expected, the correction gives more importance to the excimer. The monomer
is still the dominant component at 87 K after correction but now contributes
50% of the light at this temperature.

Figure 14: QE-corrected contribution to the total light from each component of
P15 fluorescence decay. Total excimer represents the sum of the short excimer
and the long excimer contribution. Right: Ratio of the total excimer intensity
to the total monomer intensity after PMT QE correction.

###  5.5 Discussion

Some general trends can be observed in all samples. The light yields of our
pyrene samples all increase when temperature goes down (Fig. 11). This has
been observed for pyrene in PMMA as well [17]. Overall, we attribute this to
the shutting down of non-radiative decay channels. The light yields are
between 35% and 50% of TPB at all temperatures (Fig. 11) [25]. Regarding the
time constants, all increase during cooling (Fig. 12), consistent with other
measurements of pure pyrene crystals [15] and of pyrene in PMMA [17], and also
consistent with pyrene in solutions at higher temperatures [14]. We attribute
this as well to the shutting down of non-radiative decay channels during
cooling. Fluorescence profiles were fitted with two decay time constants, a
short one between 120 ns and 160 ns at 4 K and a long one between 300 ns and
350 ns at the same temperature (Fig. 12). This is generally slower than pyrene
in PMMA [17], and significantly slower than the nanosecond timescale of TPB
[25, 10].

As for the effect of concentration and purity, at fixed purity, higher
concentration leads to higher light yield (Fig. 11). At a fixed concentration
of 15%, the highest purity sample (P15) has the highest light yield (Fig. 11).
This would be consistent with the main obstacle to fluorescence being
quenching impurities. However, the trend is reversed between the P1598 and
P1599 samples, possibly because the nominal purities are only lower limits on
the purity. The purer pyrenes (P12, P15) tend to have a larger fraction of
long decays (Fig. 12). This is consistent with a smaller number of quenching
sites from impurities. However, it is important to note that the two least
pure samples coatings were applied using a nylon bristled brush while the two
other samples films were applied with a syringe. This might contribute to the
observed behaviours.

Sample P15 was studied in more detail. As the temperature decreases, the
monomer contribution to the light yield increases, whereas that of the excimer
increases then decreases (Fig. 10). Previous work on excimers in pyrene
crystals down to 100 K [15] shows the same trend. This behaviour is consistent
with the first part of cooling being dominated by the reduction in non-
radiative decays, and the second part being dominated by a drop in exciton
migration, making it harder for dynamic excimer to form. Our excimer-over-
monomer intensity ratio decreases when cooling down, as is broadly observed
for pyrene in PMMA [17]. Below $\sim 100$ K, the monomer dominates the
emission.

Like the overall time constants, those of the monomer and excimer increase
when temperature decreases (Fig. 13). Again, it is consistent with non-
radiative decay channels shutting down during cooling. We observe no trend in
the behavior of $q$ as a function of temperature (Fig. 13). This is somewhat
surprising, as by definition (Sec. 4.5.2), $q$ should depend on non-radiative
decay rates. Alternatively, the long, $3.7\ \mu$s, window used in our analysis
drowns out the information on $q$. Lastly, our pulse shapes evidence (Fig. 8)
the presence of both static and dynamic excimer [20], respectively forming
faster and slower than our time resolution (FWHM of the instrument response in
6 ns).

##  6 Conclusion

This study considered four pyrene-doped polystyrene coated acrylic samples
which had various concentrations and purities of pyrene. The analysis of these
samples consisted of a characterization of their fluorescent response to UV
light from 300 K to 4 K, in terms of the light yield and decay times. At
temperatures from 300 K to 4 K, depending on the pyrene sample, the light
yield increased between 30% to 45%. This meant that the relative light yield
at 4 K compared to evaporated TPB was at least 40%. The decay profile of
pyrene was modelled by two exponential terms: a short decay between 120 ns and
160 ns at 4 K, and a long decay between 300 ns and 350 ns at 4 K. All time
constants increased when cooling down and are significantly longer than the
nanosecond scale TPB decay. At fixed purity, the highest pyrene concentration
sample gave the highest light yield. At fixed concentration, the highest
pyrene purity sample gave the highest light yield. The increase in purity was
also found to lead to a higher contribution from the longer time constant
decay to the overall decay at all temperatures.

One of the samples was examined in more detail by monitoring the monomer and
excimer components separately using filters. The excimer dominates the light
yield above $\sim 87$ K, the monomer dominates below that temperature. The
overall behaviour is qualitatively consistent with a reduction in non-
radiative channels at low temperature. For the excimer, below 87 K, the
results are consistent with a drop in exciton migration.

These pyrene-polystyrene films were developed to act as wavelength shifters
with slow time constants and high light yield. When these coatings are placed
on specific detector components, these properties of pyrene films can help to
provide additional pulseshape discrimination. Such films have been selected by
the DEAP collaboration to deal with surface $\alpha$ events in certain regions
of the liquid argon detector [12]. Since the surface $\alpha$ background is a
limiting background, the ability to discriminate these events increases the
discovery potential of the detector. The high light yield and slow decays over
the 4–300 K temperature range also makes these pyrene-polystyrene films
applicable to background mitigation or event type identification in other
liquid argon detectors, in other noble liquid and noble gas-based detectors,
and, more generally, in liquid scintillator detectors.

##  7 Acknowledgments

Funding in Canada has been provided by NSERC through SAPPJ grants, by CFI-LOF
and ORF-SIF, and by the McDonald Institute. M.K. is supported by the
International Research Agenda Programme AstroCeNT (MAB/2018/7) funded by the
Foundation for Polish Science (FNP) from the European Regional Development
Fund and by the EU’s Horizon 2020 research and innovation program under grant
agreement No 962480 (DarkWave). Dr. T. R. Pollmann from NIKHEF and TU München
provided comments on a draft of this work. Dr. M. Hamel of CEA Saclay provided
insight into the properties of pyrene. Queen’s NSERC USRA summer student D.
Garrow contributed to the characterization of the instrument response.

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##  Appendix A Calculation of monomer pulse integral

The integral of the non-exponential pulse shape (Eq. 4.5) can be obtained as
follows:

$\displaystyle\int_{0}^{+\infty}i_{m}(t)*E(t)dt$
$\displaystyle=\frac{N_{1}^{\prime}}{\tau_{1}^{\prime}}\int_{0}^{+\infty}e^{-\tfrac{t}{\tau_{1}^{\prime}}-2q\sqrt{\tfrac{t}{\tau_{1}^{\prime}}}}~{}dt$
(A.1)
$\displaystyle=2N_{1}^{\prime}\int_{0}^{+\infty}xe^{-x^{2}-2xq}~{}dx\mbox{
with }x=\sqrt{t/\tau_{1}^{\prime}}$
$\displaystyle=2N_{1}^{\prime}\left(\frac{1}{2}-q\int_{0}^{+\infty}e^{-x^{2}-2xq}~{}dx\right)\mbox{
integrating by parts}$
$\displaystyle=2N_{1}^{\prime}\left(\frac{1}{2}-qe^{q^{2}}\int_{0}^{+\infty}e^{-(x+q)^{2}}~{}dx\right)$
$\displaystyle=N_{1}^{\prime}\left(1-qe^{q^{2}}\sqrt{\pi}\left[1-{\mbox{erf}}(q)\right]\right)\mbox{
after }u=x+q$

where we have used the error function:
$\mbox{erf}(x)=\frac{2}{\sqrt{\pi}}\displaystyle\int_{0}^{x}e^{-t^{2}}~{}dt$


# Dynamic and Static Object Detection Considering Fusion Regions and Point-
wise Features

Andrés E. Gómez H.1, Thomas Genevois1, Jerome Lussereau1 and Christian
Laugier1 *This work was supported by the FUI STAR project1 Authors are with
Univ. Grenoble Alpes, INRIA Grenoble Rhône-Alpes, Chroma Team, 38334
Montbonnot, France. Correspondence<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Object detection is a critical problem for the safe interaction between
autonomous vehicles and road users. Deep-learning methodologies allowed the
development of object detection approaches with better performance. However,
there is still the challenge to obtain more characteristics from the objects
detected in real-time. The main reason is that more information from the
environment’s objects can improve the autonomous vehicle capacity to face
different urban situations. This paper proposes a new approach to detect
static and dynamic objects in front of an autonomous vehicle. Our approach can
also get other characteristics from the objects detected, like their position,
velocity, and heading. We develop our proposal fusing results of the
environment’s interpretations achieved of YoloV3 and a Bayesian filter. To
demonstrate our proposal’s performance, we asses it through a benchmark
dataset and real-world data obtained from an autonomous platform. We compared
the results achieved with another approach.

##  I INTRODUCTION

Autonomous Vehicles (AV) will only become a success if people accept them.
However, as Thomas et al. concluded in [1], some persons still do not believe
in Av’s performance. In fact, behind the AV development still exists several
technical challenges such as software complexity, real-time data analytics,
testing, and verification [2]. Nonetheless, a first step to win the users’
confidence can be to define new models for the AV’s perception-system that
improve its environment understand.

Different mathematical models integrate the perception system. Some of them
permit the AV to find, classify and track several objects. The models decode
sensor data collected from the physical world based on some requirements.
Therefore, the sensors are a central part of the perception system. However,
according to the sensor type, the model outcome could present some troubles.
For example, an RGB camera sensor generally is a common choice given its cost
and the significant amount of information available inside its field of view.
Nevertheless, This sensor is vulnerable to light changes and weather
conditions. Consequently, the models have to consider fusing data from
different sensors to obtain a result free of a single sensor’s limitations.

Critical models for the safety around an AV can improve their results by
fusing information from different sensor sources. Object detection models are
an example of it. By default, the development of some object detection models
considers only the use of RGB cameras. In that models, the image detections
are limited only to located and classified the objects inside a camera field
of view. However, Lidar measures from the environment could help obtain more
information concerning the same object detected by the RGB cameras (e.g.,
object position 2D).

We propose a new approach to detect dynamic and static objects in an urban
environment. Our approach also gets other characteristics from the objects
detected, such as position, velocity, and heading. We found the objects’
information fusing the environment’s interpretations from two different
approaches. The first approach is the YoloV3 object detector [3]. YoloV3
obtains a likely classification and position from each object inside an image
achieved with an RGB camera. The second approach is the Bayesian filter known
as the Conditional Monte Carlo Dense Occupancy Tracker(CMCDOT)[4]. This
approach achieves dynamic and static information from the urban environment
through a lidar sensor. Both approaches work in real-time. Our main
contributions can be summarized as follows:

  * •

Fusion of the results achieved from an object detector and a Bayesian filter
to find dynamic and static objects in an urban environment.

  * •

To get more characteristics about the objects detected by processing point-
wise features inside fusion regions.

The paper organization has the following structure: Section II reviews the
related work. Section III details the definition of the fusion regions and the
point-wise feature processing. Section IV shows the experimental results.
Finally, section V provides concluding remarks.

##  II RELATED WORK

We review related works with a focus on object detections for AV.
Specifically, we considered the works with two essential aspects: i) the
number of sensors employed, ii) and the deep learning approaches examined to
object detection. In the first aspect, we found the work presented by Liang et
al. in [5]. They considered that the range image representation is a feasible
alternative to object detection. Therefore, they developed a 3D object
detector framework called RangeRCNN, based on range images. However, despite
good performance obtained by RangeRCNN in the KITTI benchmark, it only
contemplates lidar data as input.

Arnold et al. in [6] reviewed several 3D object detection approaches on AV.
Moreover, the authors also studied different sensor technologies and standards
datasets. They consider that 2D detections on the image plane are not enough
for a safety-critical system as an AV. Therefore, The authors proved that an
object detection approach needs to fuse information from different sensors
with models executed in real-time. This fact is the basis of our proposal.

The sensors’ information and the fusion models applied in them are essential
to determine the AV’s safety. In [7], Wang, Wu, and Niu analyzed typical
sensors and several multi-sensor fusion strategies used in the last years on
AVs. The authors confirmed the need to fuse sensors’ information because it is
necessary to avoid a single sensor’s limitations and uncertainties. This
conclusion is a found fact in several papers consulted.

Rangesh and Trivedi in [8] present the $M^{3}OT$ framework. This framework can
accept an object proposal from different sensor modalities to track it. To
obtain the object track recommendations, Rangesh and Trivedi considered a
tracking-by-detection approach. This approach uses a nearly-fusion model to
associate the objects detected by each sensor. The result of the nearly-fusion
model is a merged representation from the raw sensor data.

Zhu et al. in [9] presented a literature review about several techniques used
to environment perception for intelligent vehicles. The document summarizes
different methods with their pros and cons. According to this, the deep-
learning methods adopted for the scene understanding obtained a relevant
validation. However, the authors considered that these deep-learning methods
should consider motion and depth information to improve its accuracy.
Consequently, an occupancy grid approach could be helpful to complement these
deep-learning methods.

For the second aspect, it was possible to find different deep learning
approaches used on object detection problems. For example, Pang and Cao in
[10] analyzed some object detection methods based on deep learning. They
compare typical CNN-based architectures focused on a specific use case,
pedestrian detection. One conclusion of this work is that object detection
using deep-learning approaches has several challenges. The occlusion, scale
variation, and deformation in the objects are some of them.

In [11], Zhao et al. provided the result of a systematic review, which
analyzed object detection using deep-learning models on different
applications. They compared each model through experiments. According to the
experiments’ results, the authors also concluded two main challenges on object
detection. The first one is real-time object detection, and the second one is
to extend the classical methods for 2D to 3D object detection.

Additionally, we did find some proposals where the authors considered deep-
learning approaches for the fusion of sensors’ information. For example, Nobis
et al. in [12] proposed the fusion between radar and camera sensor data using
a neural network approach. Their proposal, knowledged as Camera Radar Fusion
Net (CRF-Net), merges a 2D object detector with projected sparse radar data in
the image plane. Moreover, CRF-Net filters the data obtained in the image
plane to improve the object detection performance. However, CRF-Net does not
work in real-time.

In an AV, the perception system not only needs to detect the objects around
the urban environment. This system need also consider if the object is moving
or not. Given this fact, Siam et al. in [13] proposed a new model that
combines motion and appearance cues using a single convolutional network.
Therefore, the outcome obtained with the Moving Object Detection Network
(MODNet) is the static/moving classification for objects. Moreover, another
contribution of this work is the KITTI MOD dataset. This dataset acts as a
benchmark on motion detection on the KITTI dataset.

Finally, Liang et al. in [14] exploited multiple related tasks for 3D object
detection using multiple sensors. Moreover, They proposed to predict dense
depth from multiple sensor data and use this result to find near
correspondences between multiple sensor feature maps. These predictions are
possible through a network architecture that fuses the detections from a LiDAR
point cloud and an RGB image. This network architecture considers 2D and 3D
object detection, ground estimation, and depth completion.

After all the works analyzed previously, it is clear that object detection on
AV is not a trivial problem. Therefore, it is needed to obtain and process
enough scene information from diverse sensor sources. Moreover, the fusion
models used to merge all the information obtained must have a real-time
performance. Consequently, combining distinct methodologies like deep-learning
and Bayesian filters, it is possible to complete these requirements. Our paper
proposes a fusion model that combines the YoloV3 object detector with an
occupancy map obtained from the CMCDOT approach. The following section
explains in detail the methodology used for our proposal.

##  III METHOD

Figure 1: The figure shows the methodology’s block diagram used in our
proposal. The input part obtains raw data from an urban scene using two
sensors: lidar and an RGB camera. Eventually, in the object detection part, we
utilized two approaches to look for the objects in front of the Ego-car:
YoloV3 and CMCDOT. Then, we merge the result take from YoloV3 and CMCDOT
approaches in the Fusion model part. Afterward, we processed the previous
area’s outcome in the object information part to found the classification,
motion estimation, position, velocity, and heading from the objects222The
velocity and heading info are only for dynamic objects.. Finally, we present
all the information found in the result part.

Based on the evidence found in the related works, we define the methodology
used in our proposal. It is composed of five parts described in the block
diagram of figure 2. In this figure, it is possible to observe an ordered
sequence of subprocesses connected between each section of the block diagram.
In the remainder of this section, we will explain each subprocess and its
implications in the methodology.

###  III-A Input

An object detection approach needs to obtain information from distinct sources
to warranty an adequate performance over different situations. Considering
this fact, we decided to use an RGB camera and a lidar sensor to object
detection. These two kinds of sensors have been used in several works from
state of the art, mainly because each one supports the other in its functional
disadvantages. One instance could be RGB cameras’ problem with lack of depth
information or lidar’s issue with the loss of object details in the point
cloud. Moreover, raw data information about these two sensors is available in
diverse datasets like in [15] or [16].

###  III-B Object detection

We used the two sensors’ information as inputs for the YoloV3 and the CMCDOT
approaches. YoloV3 is an object detector that takes the RGB camera images to
classify and locate the objects presented inside the image considering a
probability. The main reason to have chosen this deep-learning approach is its
real-time operation capability. On the other hand, The CMCDOT framework does
not identify the object in the urban scene. However, The CMCDOT represents the
environment around the Ego-car through an occupancy grid map. This map is a
grid of cells, where each cell can identify occupancy states(i.e., static-
object, dynamic-object, empty, and unknown) over time. Specifically, the
static-object and dynamic-object states let us know more characteristics about
the scene’s objects, such as their position, motion estimation, etc. Finally,
the achievement of the occupancy grid map is also in real-time based on the
lidar data.

###  III-C Fusion model

In this part, we merged the outcomes from YoloV3 and the CMCDOT approaches to
have a single representation from the environment. However, the result
achieved from these two approaches is not in the same frame. Therefore, we had
to align the results applying projective transformations. Our proposal
projected the occupancy grid map accomplished by CMCDOT onto the image
processed by the YoloV3 approach. In [17], over section III-B, it is possible
to find all the mathematical argumentation used to develop the projective
transformations. Figure 2 shows the result obtained after aligning the outcome
from YoloV3 and CMCDOT.

Figure 2: Urban scene representation considering the outcomes from YoloV3 and
CMCDOT approaches. The green rectangles represent the bounding boxes generated
by YoloV3. The Dynamic/Static points are the information produced by the
CMCDOT approach after the projective transformation. Finally, the red
rectangles represent the fusion regions.

We developed a fusion model that defines several regions of interest in the
image to look for dynamic/static points inside them. We denominated those
regions as fusion regions (see Fig. 3). Equation 1 describes the fusion model
proposed.

${\quad
f}_{m}\left({B}_{min,max},{p}_{i}\right)=\begin{cases}1,{x}_{min}<{x}_{i}<{x}_{max}\\\
\qquad and\quad{Y}_{high}<{y}_{i}<{Y}_{low}.\\\ 0,other\quad
case.\end{cases}\\\ $ (1)

where,

$\begin{matrix}{Y}_{high}\quad=\quad\frac{((5*{y}_{max})-{y}_{min})}{4}\\\ \\\
{Y}_{low}\quad=\quad\frac{((3*{y}_{max})+{y}_{min})}{4}\end{matrix}$

According to equation 1, the fusion model ${f}_{m}()$ depends from the
bounding boxes points(see Fig. 3) ${B}_{min}({x}_{min},{y}_{min})$,
${B}_{max}({x}_{min},{y}_{min})$, and the dynamic/static points(see Fig. 2)
${p}_{i}({x}_{i},{y}_{i})$. Furthermore, the ${f}_{m}()$ delimit the fusion
regions(see Fig. 3) among ${x}_{min}$, ${x}_{max}$, ${Y}_{high}$, and
${Y}_{low}$. When ${f}_{m}()$ found some points ${p}_{i}$ inside the fusion
region, it classifies that point-set with the same label of the object
detected by YoloV3. Otherwise, ${f}_{m}()$ discards the fusion regions where
it did not find some point ${p}_{i}$.

Figure 3: Definition of a fusion region regarding the bounding boxes obtained.
The cyan rectangle highlights a vehicle detected. The green points represent
the main bounding box points, ${B}_{min}$ and ${B}_{max}$, got from YoloV3.
${Y}_{high}$ and ${Y}_{low}$(i.e., defined in Equation 1) are the high and low
vertical thresholds used to determine the red rectangles representing the
fusion regions.

###  III-D Object information

The outcome achieved in the fusion model part only classifies a point-set
projected from an occupancy grid map. However, this point-set maintains a
direct correspondence with the occupancy grid map’s information. Therefore, it
is possible to obtain the motion estimation, position 2D, velocity 2D, and
heading from the point set classified. We called this procedure Point-wise
features processing (PFP). Equation 2 describes the information related by
each point belonging to the point-set classified.

$p_{i}^{c}=\left\\{x_{i},\quad y_{i},\quad x_{i}^{o},\quad y_{i}^{o},\quad
vx_{i}^{o},\quad vy_{i}^{o}\right\\}$ (2)

In equation 2, the character ${p}_{i}^{c}$ defines one point $i$ belonging to
the point-set classified $c$. This point contains the coordinates ${x}_{i}$
and ${y}_{i}$ used in ${f}_{m}()$. Subsequently, the symbols ${x}_{i}^{o}$ and
${y}_{i}^{o}$ define the position 2D of the point ${p}_{i}^{c}$ over the
occupancy grid map $o$. Finally, The denotations ${vx}_{i}^{o}$ and
${vy}_{i}^{o}$ represent the velocity 2D of ${p}_{i}^{c}$ over $o$. Those
parameters also let us find the dynamic object’s heading in the scene.
${vx}_{i}^{o}$ and ${vy}_{i}^{o}$ are equal zero when the point-set belong to
a static-object.

Considering all the features contained in the points ${p}_{i}^{c}$ classifies
by ${f}_{m}$, the PFP begins to look for more details of the objects detected.
For instance, the PFP defines the object motion estimation considering the
cardinal number from the dynamic/static points-sets found in the fusion
region(see Fig. 2). The higher cardinal value will determine the motion
feature. Eventually, to find the object position, the PFP consider all the
values ${p}_{i}^{c}({x}_{i}^{o},{y}_{i}^{o})$ from the point-set to compute
their median value ${p}_{m}^{c}({x}_{m}^{o},{y}_{m}^{o})$. Similarly, to find
the dynamic object velocity, the PFP also contemplates all the values
${p}_{i}^{c}({vx}_{i}^{o},{vy}_{i}^{o})$ from the point-set to obtain a median
value ${p}_{m}^{c}({vx}_{m}^{o},{vy}_{m}^{o})$. Finally, for a dynamic object,
the PFP computes the object’s heading based on the arc tangent of
${vy}_{m}^{o}/{vx}_{m}^{o}$.

###  III-E Result

We show the results obtained from the object information part through a
Graphical User Interface (GUI) and ROS messages. Figure 4 shows some vehicles
detected by our proposal in the GUI. While the GUI shows the result, Our
algorithm also publishes ROS messages describing the motion estimation plus
the object classification(e.g., Dynamic car), position 2D, velocity 2D, and
heading. The results published in ROS corresponding to each object detected in
front of the Ego-car over time.

Figure 4: Graphical user interface used to present the result achieved with
our proposal. The cars detected inside the blue bounding boxes are static
objects in the urban scene. On the other hand, the only dynamic object in the
environment is the vehicle detected by the green bounding box.

It is essential to highlight that we didn’t consider the object’s position,
velocity, and heading in the experiment results. The main reason is that the
dataset used as a benchmark doesn’t provide this type of information about the
objects.

##  IV EXPERIMENTS

This section describes the datasets used, the experimental setup contemplated,
and the results obtained with our object detection approach.

###  IV-A Datasets

We evaluated our approach using the KITTI [15] and the KITTI MOD [13]
datasets. We used the training and test images from KITTI to train the YoloV3
approach. Moreover, in our proposal’s evaluation, we employed lidar data from
one city category. Concerning the KITTI MOD dataset, we worked with it because
it was necessary to recognize the objects’ motion based on the KITTI dataset.
The KITTI MOD dataset has 5997 static vehicles and 2383 dynamic ones labeled.

###  IV-B Experimental Setup

To Evaluate the object detection and the motion estimation of our approach, we
used ROS to publish several urban images from KITTI MOD with their
corresponding lidar data from KITTI. We obtain this ordered image sequence and
their lidar data in our algorithm through ROS messages. Inside that urban
environment, all the objects are dynamic or static vehicles. This fact is
because the KITTI MOD dataset recognizes only vehicles’ motion. Figure 5 shows
an example of our test.

Figure 5: Example of the experiment accomplished. The orange rectangle
contains one of the dynamic vehicles in the urban scene. Otherwise, the
magenta rectangle represents the static vehicles. Blue and green rectangles in
the image illustrate the comparison between the objects labeled from the KITTI
MOD dataset and the objects detected by our proposal.

In Fig 5, it is possible to observe some dynamic and static vehicles around
the Ego-car. Furthermore, the figure also shows the application of the mean
average precision (mAP) evaluation in the experiment(i.e., blue and green
labels in Fig 6). For the mAP evaluation, we considered the open-source tool
developed in [17]. In the mAP evaluation, we examined all the vehicles inside
a longitudinal distance of thirty meters from the Ego-car. The main reason is
that we found in [18] that the YoloV3 approach has a better performance in
that distance. In [8], also is contemplated a similar longitudinal distance
for the object detections.

###  IV-C Experimental results

The result obtained based on our experiment setup are present in detail
through Tables I-II and Figure 6. Table I show the performance evaluation
results based on the precision, recall, and F1 score metrics. The four classes
obtained during the experiment also include their motion estimation. Table I
also reveals that the F1 score metric maintains a balance between the
precision and recall values obtained in the three first classes. The class
dynamicCar is the most significant example with an F1-score value of 73%.

TABLE I: Performance result of our approach. Class | Presicion | Recall | F1-score
---|---|---|---
staticVan | 83 | 75 | 79
dynamicCar | 72 | 74 | 73
staticCar | 91 | 58 | 71
dynamicVan | 100 | 33 | 50

Figure 6 shows the performance evaluation of our proposal using the mean
average precision (mAP). The graph also reports average precision (AP)
achieved by each object in the test based on their class and motion
estimation. It is possible to observe that the AP value between the classes
dynamicCar and staticCar are near. On the other hand, in Table II we compare
the mAP obtained by our approach against the MODNET [13] approach. Although we
achieved an mAP nearby, our proposal also can obtain more characteristics from
the object detected(i.e., position, velocity, and heading). It is possible to
find all the information about the experiment results in
https://gitlab.inria.fr/agomezhe/paper_results.

Figure 6: Mean average precision (mAP) result achieved in our test. The figure shows the Average Precision (AP) values(i.e., in blue bars) obtained by each object detected during the experiment, considering their class and motion estimation. TABLE II: Quantitative evaluation on KITTI MOD data of our proposal. Method | mAP
---|---
MODNET[13] | 62.57
Ours | 55.14

Finally, Figure 7 shows a demonstration example of our proposal with results
achieved. In this urban demonstration, we used sensor data from our autonomous
vehicle platform. The main objective was to test our approach considering
different urban situations under extreme weather conditions(i.e., sunny and
rainy weather). It is possible to find a video with the demonstration results
in https://youtu.be/Rd-0B0--mlc.

Figure 7: Illustration of our proposal using the autonomous vehicle platform.
The graph shows several green bounding boxes representing different dynamic
objects around our autonomous vehicle platform. The blue bounding box present
in the image exhibit only one static object.

##  V CONCLUSIONS

This paper considers detecting dynamic and static objects around an autonomous
vehicle in an urban environment. However, our proposal is not only limited to
object detection and its motion estimation. Furthermore, we also look for the
position, velocity, and heading from the objects detected. We found all these
characteristics of the objects detected in the scene, considering the fusion
of the YoloV3 and CMCDOT approaches’ outcomes. That information lets to define
the fusion regions and the point-wise features utilized in our approach.

The results obtained show that our proposal is nearby to the performance
achieved by the work used as a reference. Therefore, to improve our approach’s
performance, future works are focus on taking some actions like updating the
YoloV3 to YoloV4 object detector. Moreover, it will also be necessary to train
the YoloV4 with a new balanced dataset. Finally, even when all our approach
was optimized using a GPU, we will need to compensate our hardware
disadvantage compared to the referenced work.

## ACKNOWLEDGMENT

This work has been supported by the French Government in the scope of the FUI
STAR project. We also would like to thank Özgür Erkent for his meaningful
technical discussions and suggestions.

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