Objects Motion Detection in Domain-adapted Assisted Driving
Francesco Rundo
1 a
, Roberto Leotta
and Sebastiano Battiato
2 b
STMicroelectronics, ADG Central R&D, Catania, Italy
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
ADAS, Automotive, Deep Learning, Road Classification, Intelligent Suspension.
The modern Advanced Driver Assistance Systems (ADAS) contributed to reduce road accidents due to the
driver’s inexperience or unexpected scenarios. ADAS technologies allow the intelligent monitoring of the
driving scenario. Recently, estimation of the visual saliency i.e. the part of the visual scene in which the driver
put high visual attention has received significant research interests. This work makes further contributions
to video saliency investigation for automotive applications. The difficulty to collect robust labeled data as
well as the several features of the driving scenarios require the usage of such domain adaptation methods.
A new approach to Gradient-Reversal domain adaptation in deep architectures is proposed. More in detail,
the proposed pipeline enables an intelligent identification and segmentation of the motion salient objects in
different driving scenarios and domains. The performed test results confirmed the effectiveness of the overall
proposed pipeline.
The term Advanced Driver Assistance Systems
(ADAS) includes different type of intelligent solu-
tions including systems providing driver assistance,
advice and warnings, self autonomous driving and
so on (Okuda et al., 2014). In this context the
car assisting-information systems or LiDAR/RADAR
based applications can be included (Spelt and Tufano,
1998). Recent ADAS technology enhancement in-
cludes Intelligent Speed Adaptation systems, colli-
sion warning systems, car driver drowsiness monitor-
ing and pedestrian tracking systems (Ogitsu and Mi-
zoguchi, 2015; Wang et al., 2019; Ganin and Lem-
pitsky, 2015). The ADAS warnings system may be
auditory, visual or haptic, covering such level of such
standard automotive (Zhan et al., 2020). Deep Learn-
ing solutions have significantly improved the ability
of algorithms to address several issues in automotive
and ADAS fields.
Often, the problem of lacking labeled data, can
impact the performance of such artificial intelligence
based solutions. To address this relevant issue, ad-
hoc intelligent domain adaptation approaches have
been implemented and published in scientific litera-
ture database (Ganin and Lempitsky, 2015). The pro-
posed pipeline embeds innovative domain adaptation
approach based on the usage of the Gradient Rever-
sal algorithm. More in detail, the authors propose an
overall ADAS system embedding a physio-based car
driver drowsiness tracking system combined with a
domain-adapted intelligent risk assessment of the as-
sociated driving scenario. Specifically, by means of
the designed innovative domain adaptation method,
the proposed pipeline will be able to detect and track
the driving motion objects, providing an associated
overall driving risk assessment. About objects motion
estimation, different solutions have been proposed. A
summary about scientific state of the arts is reported.
In (Zheng et al., 2018) the authors proposed an
approach based on odometry for object motion esti-
mation to be extended to automotive market. The re-
ported performance confirmed the effectiveness of the
implemented pipeline. In (Barjenbruch et al., 2015)
the authors implemented an interesting motion detec-
tion pipeline based on the usage of doppler effect over
radar technologies. Even the investigated approach
showed very interesting results, the drawback to need
the radar equipment was highlighted. In (Hee Lee
et al., 2013) the authors proposed a visual ego-motion
estimation algorithm for a self-driving car equipped
with a commercial multi-camera system. The results
obtained over a large dataset confirmed the robustness
of the proposed architecture (Hee Lee et al., 2013).
Rundo, F., Leotta, R. and Battiato, S.
Objects Motion Detection in Domain-adapted Assisted Driving.
DOI: 10.5220/0010973100003209
In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), pages 101-108
ISBN: 978-989-758-563-0; ISSN: 2795-4943
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Further supervised and unsupervised based deep solu-
tions were widely investigated with aim to address the
issue of the efficient driving object tracking and mo-
tion estimation (Grigorescu et al., 2019; Alletto et al.,
2018; Wang et al., 2021). Considerable interest has
been found by the unsupervised and semi-supervised
domain adaptation techniques.
In (Singh et al., 2021) the authors proposed a
semi-supervised domain adaptation approach which
leveraged limited labeled target samples with unla-
beled data to manage the distribution shift across the
source and target domains. The proposed approach
contributed significantly in bridging the domain gap
as confirmed by the experimental results reported in
(Singh et al., 2021). The survey reported in (Carr
et al., 2018) showed the considerable advantages that
domain adaptation techniques entail for ADAS and
automotive applications. About driver attention mon-
itoring systems, the authors of the pipeline herein pro-
posed have deeply investigated that issue(Vinciguerra
et al., 2018; Conoci et al., 2018; Rundo et al., 2018a;
Rundo et al., 2018b; Trenta et al., 2019; Rundo et al.,
2019a; Rundo et al., 2020a; Rundo et al., 2020b).
More in detail, the performed scientific investiga-
tion has confirmed that the car driver physiological
signals, especially the Photoplethysmography (PPG),
can be efficiently used to real-time monitoring of the
subject drowsiness (Rundo et al., 2020b; Rundo et al.,
2019b; Lee et al., 2019). The proposed full solution
can be contextualized in the intelligent driving assis-
tance approaches in different driving scenarios and
As introduced, the core of the proposed approach re-
gards a domain-adapted driving risk assessment sys-
tem for ad-hoc object motion tracking. In Fig. 1 the
overall diagram of the proposed pipeline.
The system reported in Fig. 1 allow a robust
driving risk level assessment through an intelligent
processing of the sampled driving visual frames.
The sampled driving scene frames will be processed
by ad-hoc Semantic Segmentation Fully Convolu-
tional Neural Network embedding a Gradient Re-
versal layer (SS-FCN-GRL) (Ganin and Lempitsky,
2015). Through a semantic segmentation of the cap-
tured driving visual frames, the saliency map of the
analyzed source driving scene will be reconstructed.
This saliency map will be fed as input data of the
downstream driving safety assessment sub-system to
retrieve the correlated motion dynamic.
The proposed SS-FCN-GRL architecture will be
described in detail. The designed encoder block (En-
coding) composed by 5 layers is able to process the
visual features of the captured driving frames. The
first two blocks embed (for each block) two separa-
ble convolution layers with 3 × 3 kernel filters fol-
lowed by a batch normalization, ReLU layer and a
downstream 2 × 2 max-pooling layer. The remain-
ing three blocks include two separable convolution
layers with 3 × 3 kernel filter followed by a batch
normalization, another convolutional layer with 3 × 3
kernel, batch normalization and ReLU with a down-
stream 2 × 2 max-pooling layer. The Decoder stage
of the proposed pipeline is composed as per encoder
structure i.e. up-sampling the encoded visual features
through ad-hoc decoding processing. The decoder is
composed by five blocks including 2D convolutional
layers with 3 × 3 kernel, batch normalization layers,
ReLU. Classical skip-connections through convolu-
tional block were embedded in the backbone. In the
decoder side we have interpolated such up-sampling
blocks (with bi-cubic algorithm) to adjust the size of
the generated feature maps.
To improve the domain adaptation capability, the
authors have embedded the mentioned Gradient Re-
versal Layer block (Ganin and Lempitsky, 2015) as
per Fig. 1. The designed he model works with in-
put samples x X, where X is the input space while
y (label data) from the label space Y . Let’s defined
a classification problems where Y is a finite set (Y =
{1,2,...,L}), handling any output label space. We fur-
ther assumed that there exist two distributions S(x,y)
and T (x, y) X Y, which will be referred to as the
source distribution and the target distribution (or the
source domain and the target domain). Both distribu-
tions were assumed as unknown and specifically S is
“shifted” from T through a not specified domain re-
mapping. Our objective function is to predict labels y
given the input x for the target distribution. At training
time, we supposed to collect large training samples
} from both the source and the target do-
mains distributed according to the defined statistical
distributions S(x) and T (x).
We denote with d
the binary variable (domain la-
bel) for the i-th example, which means that (x
if d
= 0) or (x
T (x) if d
= 1). We now define a
custom deep feed-forward architecture which for each
input x predicts its label y Y and its domain label
d {0,1}. The authors assumed that the input x is
re/mapped through the function G
(a feature extrac-
tor) to a D-dimensional feature vector f R
. The
proposed feature mapping includes feed-forward lay-
ers and we denote the vector of parameters of all lay-
ers in this mapping as θ
, i.e. f = G
). Then,
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering
Figure 1: The proposed domain adapted motion assessment pipeline.
the feature vector f is mapped by a mapping G
predictor) to the label y, and we denote the parameters
of this mapping with θ
. Finally, the same feature vec-
tor f is mapped to the domain label d by a mapping
(domain classifier) with the parameters θ
(Fig. 1).
During the learning session, the implemented
deep system tries to minimize the label prediction loss
on the labeled part (i.e. the source part) of the train-
ing set. At the same time, the parameters of both the
feature extractor and the label predictor are thus opti-
mized in order to minimize the empirical loss for the
source domain unlabeled samples. In the following
section, a mathematical formalization of the proposed
GRL approach:
) =
) =
) λ
where L
(·,·) is the loss for the label prediction while
(·,·) is the loss for the domain classification. The
terms L
and L
denote the corresponding loss func-
tions evaluated at the i-th training input frames. Based
on our proposed architecture, we are seeking the pa-
rameters θ
that find a saddle point of the Eq. 1:
) = argmin
) (2)
= argmax
) (3)
At the saddle point, the parameters θ
of the do-
main classifier minimizes the domain classification
loss while the parameters θ
of the label predictor
minimizes the label prediction loss. The feature map-
ping parameters θ
minimizes the label prediction
loss, while maximizing the domain classification loss
(i.e. the features are domain-invariant). The learning
rate λ modulates the two objective dynamics. The au-
thors whose designed the GRL approach have showed
that the classical Stocastic Gradient Descent (SGD)
learning is able to find the needed saddle point (Ganin
and Lempitsky, 2015).
After that SGD-based learning, the label predic-
tor y(x) = G
) can be used to predict la-
bels for samples from the target domain (and clearly
the source domain). The output of the so designed
SS-FCN-GRL is the feature saliency map of the sam-
pled driving frame. Specifically, a no-zero saliency
map will be generated in case of motion objects while
null saliency map will be generated in case of slow-
motion or static objects in the sampled driving sce-
nario frames.
Through the action of the GRL the proposed so-
lutions are able to train the deep network both with
label data and target ones (different driving scenario
frames) suitable to retrieve a robust domain-invariant
motion-objects saliency map, thus characterizing the
risk of driving accordingly.
In Fig. 2 the authors have reported some instances
of the so generated saliency maps for moving and
slow-motion objects in the analyzed driving scenario.
The proposed SS-FCN-GRL architecture has been
validated and tested on the DHF1K dataset (Min and
Corso, 2019). The proposed solution has showed
acceptable performance on DHF1K dataset (Rundo
Objects Motion Detection in Domain-adapted Assisted Driving
Figure 2: Intelligent Driving Objects Tracking and Motion
Detection: (a) High moving saliency objects tracking; (b)
Slow motion objects tracking and detection (Null saliency
et al., 2019b) (Area Under the Curve: 0.892; Sim-
ilarity: 0.415; Correlation Coefficient: 0.480; Nor-
malized Scanpath Saliency: 2.598) with respect to
similar intelligent backbones. A careful porting of
the proposed deep solution as per Fig. 1 is running
over ad-hoc hardware with graphic/AI accelerations
and provided by STMicroelectronics (MCUs frame-
work based STA1295A Accordo5 and SPC5x Chorus
devices) (Rundo et al., 2021).
As introduced, the proposed ADAS solution embeds
a robust intelligent car driver drowsiness monitoring
system. Specifically, we proposed a car-driver at-
tention level monitoring based on the usage of the
driver’s Photoplethysmographic (PPG) signal.
The PPG signal can be considered as a less-
invasive physio-signal suitable to monitor cardiovas-
cular dynamics of a human subject. Both heart pulse
and respiratory rate as well as vascular and cardiac
disorders may be monitored by means of ad-hoc anal-
ysis of the PPG dynamic (Ganin and Lempitsky,
2015). Through the PPG signal the authors were
able to perform less-invasive measure of the subject
blood volume changes. A classical PPG waveform
embeds a pulsatile (‘AC’) physiological signal which
is correlated to cardiac-synchronous changes in the
blood volume superimposed with a slowly varying
(‘DC’) component containing lower frequency sub-
signals correlated to respiration and other physiologi-
cal parameters. The change in volume caused by the
periodic heart pressure pulses can be tracked by il-
luminating the skin of the subject and then by mea-
suring the amount of light either transmitted or back-
scattered by means of ad-hoc combined detector (Og-
itsu and Mizoguchi, 2015; Wang et al., 2019). More
detail about PPG patter formation in (Okuda et al.,
2014; Spelt and Tufano, 1998; Panagiotopoulos and
Dimitrakopoulos, 2019; Ogitsu and Mizoguchi, 2015;
Wang et al., 2019; Ganin and Lempitsky, 2015; Zhan
et al., 2020). For the proposed pipeline, the authors
have used the PPG sampling embedding a Silicon
Photomultiplier (SiPM) device provided by STMicro-
electronics (Vinciguerra et al., 2018; Conoci et al.,
2018; Rundo et al., 2018a).
The proposed PPG sensing probes includes a large
area n-on-p Silicon Photomultipliers (SiPMs) fabri-
cated at STMicroelectronics (Conoci et al., 2018;
Rundo et al., 2018a). 4.0 × 4.5 mm
and 4871 square
microcells with 60 µm pitch. The devices have a ge-
ometrical fill factor of 67.4% and are packaged in a
surface mount housing (SMD) with about 5.1 × 5.1
total area (Conoci et al., 2018). We propose
the usage of Pixelteq dichroic bandpass filter with a
pass band centered at about 840 nm nm with a Full
Width at Half Maximum (FWHM) of 70 nm and an
optical transmission higher than 90 95% in the pass
band range was glued on the SMD package by using
a Loctite 352TM adhesive. With the dichroic filter at
3V-OV the SiPM has a maximum detection efficiency
of about 30% at 565 nm and a PDE of about 27.5%
at 830 nm (central wavelength in the filter pass band).
We have applied a dichroic filter to reduce the absorp-
tion of environmental light of more than 60% when
the detector works in the linear range in Geiger mode
above its breakdown voltage (27 V).
As described, the so designed PPG probe embeds
a set of OSRAM LT M673 LEDs in SMD package
emitting at 830 nm and based on InGaN technology
(Conoci et al., 2018). The used LEDs devices have an
area of 2.3 × 1.5 mm
, viewing angle of 120°, spec-
tral bandwidth of 33 nm and lower power emission
(mW) in the standard operation range. The authors
designed an embedded motherboard populated by a
4 V portable battery, a power management circuits,
a conditioning circuit for output SiPMs signals, sev-
eral USB connectors for PPG probes and related SMA
output connectors (Conoci et al., 2018; Rundo et al.,
2018b). We designed to embed several PPG sensing
probes on the car steering.
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering
Figure 3: The designed PPG sensing platform.
In Fig. 3 we report an overall scheme of the pro-
posed PPG sensing framework. The filtering and sta-
bilization of the collected raw PPG signal will be
performed by the developed algorithms running as
firmware in the SPC5x Chorus MCU (Conoci et al.,
2018; Rundo et al., 2018b; Trenta et al., 2019; Rundo
et al., 2019a; Rundo et al., 2020a; Rundo et al.,
2020b; Rundo et al., 2019b).
The designed hyper-filtering approach (Rundo
et al., 2018b; Rundo et al., 2019b) will be applied
to the collected steady-state PPG raw data in order to
retrieve such discriminative features to be correlated
to the driver attention level.
More in detail, the idea inside the hyper-filtering
approach was inspired by hyper-spectral method usu-
ally applied to 2D data (Rundo et al., 2019b). Basi-
cally, the authors investigated the discrimination level
of the features retrieved by the ”hyper-filtering” of
the source car driver PPG signal. More in detail, in-
stead of applying a single filter setup (low pass and
high pass) having a well-defined cut-off frequency,
we have analyzed a range of dynamic frequencies in
which the PPG signal shows useful information. Con-
sidering that the useful frequency range is included in
the 0.5 10Hz, we have investigated the performance
of an hyper-filtered PPG-based classification system
in which the signal frequency spectrum (0.5 10Hz)
was divided into several sub-bands. We have config-
ured two spectral layers of hyper-filtering layer. A
first layer changes the frequencies in the low-pass fil-
ter maintaining instead the cut-off frequency of the
high-pass filter (Hyper low-pass filtering layer) and
vice versa a layer that changes the cut-off frequencies
of the high-pass filter while maintaining fixed the fre-
quency setup of the low pass filter ((Hyper high-pass
filtering layer). Due to an efficient noise-modulations
in the bandwidth, we adopted the Butterworth filter
types in both layers of Hyper filtering (Rundo et al.,
2020a; Rundo et al., 2020b; Rundo et al., 2019b).
Through a Reinforcement Learning algorithm (Rundo
et al., 2020b; Rundo et al., 2019b) we optimized the
setup of hyper-filtering layer to be applied to the col-
Figure 4: The proposed downstream PPG deep 1D-CNN
lected car driver PPG signal. This setup is reported in
the following Table 1 e Table 2.
Table 1: Hyper Low-pass filtering setup (in Hz).
F F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11
HP 0.5 / / / / / / / / / /
LP 0.0 1.2 3.3 3.5 3.6 3.8 4.0 4.2 5.0 5.1 6.1
Table 2: Hyper High-pass filtering setup (in Hz).
F F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11
HP 0.0 1.2 2.3 2.6 3.1 3.5 4.0 4.3 5.0 5.5 6.2
LP 0.6 / / / / / / / / / /
At this point, once the hyper-filtering configura-
tion has been completed, the car driver PPG raw sig-
nal that is gradually sampled will be processed ac-
cordingly to the so configured hyper-filtered frame-
work. For each sample of the single PPG waveform,
a dataset of hyper filtered signals will be generated,
each having a temporal dynamics represented by the
time-dynamic of that signal-sample. Formally, if we
indicate with W
(t,k) the single segmented PPG
compliant waveform of each hyper-filtered PPG time-
serie, we proceed computing for each sample s(t, k) of
the waveform a signal-pattern depending on how that
signal samples s(t,k) changes in intensity in each of
the previously generated hyper-filtered signals.
Through the above detailed hyper-filtered
pipeline, a large dataset of hyper-filtered signals will
be generated and collected. The above RL algorithms
confirmed that a well optimization is reached by
11 sub-bands of hyper-filtering frequencies. The so
generated signal-patterns will be fed into the Deep
Learning block as described in the Fig. 4.
The signal-patterns generated by the previous
hyper-filtering PPG processing pipeline (Rundo et al.,
2019b) will be preliminary encoded by a Long-
Short-Term Memory (LSTM) network. The LSTM
composed by 6 layers of vanilla-unit backbone
(Trenta et al., 2019), is able to encode the hyper-
filtered signal-patterns in order to provide a folding-
Objects Motion Detection in Domain-adapted Assisted Driving
embedding of the input signals. These features will
be fed as input to the deep 1D downstream classifier
(Rundo et al., 2018b; Trenta et al., 2019). The Deep
1D Temporal Dilated Convolutional Neural Network
(1D-CNN) with residual block (Rundo et al., 2020b).
Specifically, we have implemented a 1D-CNN em-
bedding 36 blocks with a downstream softmax layer.
The output of the deep network is a classification of
the input hyper-filtered PPG patterns i.e. a classifica-
tion of drowsy (0.0, 0.5) or wakeful (0.51,1.0) driver.
Each of the 1D-CNN block consists of a dilated
convolution layer having 3 × 3 kernel filters, a spa-
tial dropout layer, another dilated convolution layer,
ReLU layer and a final spatial drop. The dilation size
start from 2 and increase (power of 2) for each block
till the max value of 32. A softmax layer completes
the proposed pipeline. The so designed Deep Learn-
ing framework is able to estimate and monitor the car
driver drowsiness level. As reported in Fig. 4, the pro-
posed deep classifier is running over the STA1295A
Accordo5 MCU with ad-hoc Graphics accelerator and
Linux YOCTO and OpenCV based software frame-
work (Rundo et al., 2020b; Rundo et al., 2019b).
We tested the proposed pipeline, firstly validating
each of the implemented sub-systems and then ar-
ranging a combined testing scenario. Specifically,
we have considered the following risk assessment
in relation to the tracking of the salient moving ob-
jects: detected no-zero map for salient moving ob-
jects (medium/high driving risk) against a scenario
with detected zero-map slow moving salient objects
with associated null generated saliency map (low risk
driving scenario).
Therefore, the proposed full pipeline provide
an overall driving risk assessment comparing the
saliency-motion-based risk evaluation with the PPG
physio-based drowsiness monitoring retrieving if that
attention level is adequate or not.
More in detail, if high or medium risk level is de-
tected, the proposed driving monitoring system will
check if the designed 1D-TCNN detects a correspond-
ing ”wakeful driver” classification. Otherwise, acous-
tic alert-signal will be generated. In the scenario in
which the driver’s PPG signal is not available for
some reasons, the authors have developed a Visual-
to-PPG replacement algorithm (Trenta et al., 2019).
About the physio-based car driver drowsiness assess-
ment, we have validated the proposed pipeline by col-
lecting several PPG measurements of different sub-
Table 3: Car Driver Drowsiness monitoring performance.
Driver Drowsiness Monitoring
Proposed 99.76% 99.89%
CNN w/o
98.71% 99.03%
(Rundo et al., 2019b) 96.50% 98.40%
jects in different scenarios (Drowsy driver vs Wakeful
driver) under authorization of the Ethical Committee
CT1 authorization Nr. 113 / 2018 / PO. The PPG
sampling sessions have been supervised by experi-
enced physicians. We have collected data from 70 pa-
tients with different ages, sex, and so on (Rundo et al.,
2019a). We have used the herein introduced PPG
hardware setup with sampling frequency of 1 kHz.
For each condition (Drowsy vs Wakeful subject) we
have collected 5 minutes of PPG signals. All ac-
quired PPG time-series have been arranged as fol-
low: 70% for the training and validation phase of the
Deep learning framework while the remaining 30%
have been used for testing. We have used different
driving scenarios: some labeled driving scenarios and
some unlabeled target domain scenarios for testing
the proposed GRL approach. The results reported in
Table 3 confirmed that the physio-sensing system for
car driver drowsiness monitoring outperformed simi-
lar pipelines in terms of accuracy.
The SS-FCN-GRL and the 1D-TCNN have been
trained with a classic SGD algorithm with dropout
factor of 0.75 and initial learning rate of 0.001. The
LSTM layer was trained with an initial learning rate
of 0.002. The Table 3 shows the performance of
the proposed pipeline compared with similar pipeline
based on deep learning (Rundo et al., 2019b) both in
labeled and target domain adapted driving scenarios.
We consider such interval of about 8/12 seconds of
PPG sampling in order to show the near real-time
performance of the proposed pipeline. Finally, we
have tested the combined full system. Specifically, we
have validated the proposed low/high risk assessment
of the analyzed driving scenarios. As highlighted by
the experimental results reported in Table 4, the archi-
tecture that exports the domain adaptation GRL layer
shows high performance in risk assessment compared
to the benchmark architectures. The use of the GRL
significantly improves the characterization of the mo-
tion of the tracked objects and therefore the conse-
quent risk assessment (accuracy on average of 96%
against 91% of the system without GRL).
The collected performance confirmed the robust-
ness and the effectiveness of the proposed overall ap-
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering
Table 4: Proposed Intelligent Saliency-Motion Driving risk
assessment system.
Intelligent Domain Adapted Risk
Assessment Performance
Low Risk
(Static Salient Objects)
High Risk
(Salient Moving Objects)
Proposed 96.78% 96.66%
Proposed w/o
91.87% 91.09%
Classic SS-FCN
With Attention
(Rundo et al., 2021)
91.65% 90.90%
Classic SS-FCN
89.30% 90.11%
Classic SS-FCN
(Min and Corso, 2019)
87.79% 90.01%
Future works aim on embedding such features of
deep LSTM with Attention to further improve the per-
formance of overall pipeline (Rundo, 2019).
The authors thank the physiologists belonging to the
Department of Biomedical and Biotechnological Sci-
ences (BIOMETEC) of the University of Catania,
who collaborated in this work in the context of the
clinical study Ethical Committee CT1 authorization
n.113 / 2018 / PO. This research was funded by
the National Funded Program 2014-2020 under grant
agreement n. 1733, (ADAS + Project). The reported
information is covered by the following registered
patents: IT Patent Nr. 102017000120714, 24 Octo-
ber 2017. IT Patent Nr. 102019000005868, 16 April
2018; IT Patent Nr. 102019000000133, 07 January
Alletto, S., Abati, D., Calderara, S., Cucchiara, R., and
Rigazio, L. (2018). Self-supervised optical flow esti-
mation by projective bootstrap. IEEE Transactions on
Intelligent Transportation Systems, 20(9):3294–3302.
Barjenbruch, M., Kellner, D., Klappstein, J., Dickmann, J.,
and Dietmayer, K. (2015). Joint spatial-and doppler-
based ego-motion estimation for automotive radars. In
2015 IEEE Intelligent Vehicles Symposium (IV), pages
839–844. IEEE.
e, M., Exposito, E., and Ibanez-Guzman, J. (2018).
Challenges for the self-safety in autonomous vehicles.
In 2018 13th Annual Conference on System of Systems
Engineering (SoSE), pages 181–188. IEEE.
Conoci, S., Rundo, F., Fallica, G., Lena, D., Buraioli, I., and
Demarchi, D. (2018). Live demonstration of portable
systems based on silicon sensors for the monitoring
of physiological parameters of driver drowsiness and
pulse wave velocity. In 2018 IEEE Biomedical Cir-
cuits and Systems Conference (BioCAS), pages 1–3.
Ganin, Y. and Lempitsky, V. (2015). Unsupervised do-
main adaptation by backpropagation. In International
conference on machine learning, pages 1180–1189.
Grigorescu, S. M., Trasnea, B., Marina, L., Vasilcoi, A.,
and Cocias, T. (2019). Neurotrajectory: A neuroevo-
lutionary approach to local state trajectory learning for
autonomous vehicles. IEEE Robotics and Automation
Letters, 4(4):3441–3448.
Hee Lee, G., Faundorfer, F., and Pollefeys, M. (2013). Mo-
tion estimation for self-driving cars with a general-
ized camera. In Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition, pages
Lee, H., Lee, J., and Shin, M. (2019). Using wearable
ecg/ppg sensors for driver drowsiness detection based
on distinguishable pattern of recurrence plots. Elec-
tronics, 8(2):192.
Min, K. and Corso, J. J. (2019). Tased-net: Temporally-
aggregating spatial encoder-decoder network for
video saliency detection. In Proceedings of the
IEEE/CVF International Conference on Computer Vi-
sion, pages 2394–2403.
Ogitsu, T. and Mizoguchi, H. (2015). A study on driver
training on advanced driver assistance systems by us-
ing a driving simulator. In 2015 International Confer-
ence on Connected Vehicles and Expo (ICCVE), pages
352–353. IEEE.
Okuda, R., Kajiwara, Y., and Terashima, K. (2014). A sur-
vey of technical trend of adas and autonomous driv-
ing. In Technical Papers of 2014 International Sym-
posium on VLSI Design, Automation and Test, pages
1–4. IEEE.
Panagiotopoulos, I. and Dimitrakopoulos, G. (2019). Cog-
nitive infotainment systems for intelligent vehicles. In
2019 10th International Conference on Information,
Intelligence, Systems and Applications (IISA), pages
1–8. IEEE.
Rundo, F. (2019). Deep lstm with reinforcement
learning layer for financial trend prediction in fx
high frequency trading systems. Applied Sciences,
Rundo, F., Conoci, S., Battiato, S., Trenta, F., and Spamp-
inato, C. (2020a). Innovative saliency based deep driv-
ing scene understanding system for automatic safety
assessment in next-generation cars. In 2020 AEIT In-
ternational Conference of Electrical and Electronic
Technologies for Automotive (AEIT AUTOMOTIVE),
pages 1–6. IEEE.
Rundo, F., Conoci, S., Ortis, A., and Battiato, S.
(2018a). An advanced bio-inspired photoplethysmog-
raphy (ppg) and ecg pattern recognition system for
medical assessment. Sensors, 18(2):405.
Rundo, F., Leotta, R., and Battiato, S. (2021). Real-time
deep neuro-vision embedded processing system for
saliency-based car driving safety monitoring. In 2021
4th International Conference on Circuits, Systems and
Simulation (ICCSS), pages 218–224. IEEE.
Objects Motion Detection in Domain-adapted Assisted Driving
Rundo, F., Petralia, S., Fallica, G., and Conoci, S. (2018b).
A nonlinear pattern recognition pipeline for ppg/ecg
medical assessments. In Convegno Nazionale Sensori,
pages 473–480. Springer.
Rundo, F., Rinella, S., Massimino, S., Coco, M., Fallica, G.,
Parenti, R., Conoci, S., and Perciavalle, V. (2019a).
An innovative deep learning algorithm for drowsiness
detection from eeg signal. Computation, 7(1):13.
Rundo, F., Spampinato, C., Battiato, S., Trenta, F., and
Conoci, S. (2020b). Advanced 1d temporal deep di-
lated convolutional embedded perceptual system for
fast car-driver drowsiness monitoring. In 2020 AEIT
International Conference of Electrical and Electronic
Technologies for Automotive (AEIT AUTOMOTIVE),
pages 1–6. IEEE.
Rundo, F., Spampinato, C., and Conoci, S. (2019b). Ad-hoc
shallow neural network to learn hyper filtered photo-
plethysmographic (ppg) signal for efficient car-driver
drowsiness monitoring. Electronics, 8(8):890.
Singh, A., Doraiswamy, N., Takamuku, S., Bhalerao, M.,
Dutta, T., Biswas, S., Chepuri, A., Vengatesan, B., and
Natori, N. (2021). Improving semi-supervised domain
adaptation using effective target selection and seman-
tics. In Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition, pages
Spelt, P. and Tufano, D. (1998). An in-vehicle infor-
mation system for its information management. In
17th DASC. AIAA/IEEE/SAE. Digital Avionics Sys-
tems Conference. Proceedings (Cat. No. 98CH36267),
volume 2, pages I31–1. IEEE.
Trenta, F., Conoci, S., Rundo, F., and Battiato, S. (2019).
Advanced motion-tracking system with multi-layers
deep learning framework for innovative car-driver
drowsiness monitoring. In 2019 14th IEEE Inter-
national Conference on Automatic Face & Gesture
Recognition (FG 2019), pages 1–5. IEEE.
Vinciguerra, V., Ambra, E., Maddiona, L., Romeo, M.,
Mazzillo, M., Rundo, F., Fallica, G., di Pompeo,
F., Chiarelli, A. M., Zappasodi, F., et al. (2018).
Ppg/ecg multisite combo system based on sipm tech-
nology. In Convegno Nazionale Sensori, pages 353–
360. Springer.
Wang, C., Sun, Q., Guo, Y., Fu, R., and Yuan, W. (2019).
Improving the user acceptability of advanced driver
assistance systems based on different driving styles:
A case study of lane change warning systems. IEEE
Transactions on Intelligent Transportation Systems,
Wang, K., Jiasheng, N., and Yanqiang, L. (2021). A ro-
bust lidar state estimation and map building approach
for urban road. In 2021 IEEE 2nd International Con-
ference on Big Data, Artificial Intelligence and Inter-
net of Things Engineering (ICBAIE), pages 502–506.
Zhan, H., Wan, D., and Huang, Z. (2020). On the responsi-
ble subjects of self-driving cars under the sae system:
An improvement scheme. In 2020 IEEE International
Symposium on Circuits and Systems (ISCAS), pages
1–5. IEEE.
Zheng, F., Tang, H., and Liu, Y.-H. (2018). Odometry-
vision-based ground vehicle motion estimation with
se (2)-constrained se (3) poses. IEEE transactions on
cybernetics, 49(7):2652–2663.
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering