Situational Collective Perception: Adaptive and Efficient Collective
Perception in Future Vehicular Systems
Ahmad Khalil
1 a
, Tobias Meuser
1 b
, Yassin Alkhalili
1 c
, Antonio Fernandez Anta
2 d
Lukas Staecker
3 e
and Ralf Steinmetz
1 f
Multimedia Communications Lab, Technical University of Darmstadt, Darmstadt, Germany
IMDEA Networks Institute, Madrid, Spain
Stellantis, Opel Automobile GmbH, R
usselsheim, Germany
Collective Perception, Vehicular Networks, Intelligent Transportation Systems, V2X, Federated Learning.
With the emerge of Vehicle-to-everything (V2X) communication, vehicles and other road users can perform
Collective Perception (CP), whereby they exchange their individually detected environment to increase the
collective awareness of the surrounding environment. To detect and classify the surrounding environmental
objects, preprocessed sensor data (e.g., point-cloud data generated by a Lidar) in each vehicle is fed and classi-
fied by onboard Deep Neural Networks (DNNs). The main weakness of these DNNs is that they are commonly
statically trained with context-agnostic data sets, limiting their adaptability to specific environments. This may
eventually prevent the detection of objects, causing safety disasters. Inspired by the Federated Learning (FL)
approach, in this work we tailor a collective perception architecture, introducing Situational Collective Percep-
tion (SCP) based on dynamically trained and situational DNNs, and enabling adaptive and efficient collective
perception in future vehicular networks.
The number of vehicles on the roads is expected to
reach 1.8 billion worldwide by 2035 (L
offler, 2021),
therefore more crowded roads will continue to be a
common phenomenon in the future. Crowded roads
not only increase the probability of having accidents
but also reduce the overall traffic efficiency. This
highlights the necessity to develop and deploy more
intelligent systems in the vehicles, to assist the vehi-
cle’s driver. Supporting intelligent systems help in-
creasing traffic safety and efficiency, by making the
vehicles’ actions less dependent, or even fully inde-
pendent, from the driver decisions.
However, when taking over, the intelligent sys-
tems in the vehicles need to be aware of their envi-
ronment. By depending only on their onboard sen-
sors, the vehicles have limited perception of their sur-
rounding environment. Therefore, enabling commu-
nication to exchange perception data between vehi-
cles and other road users is a pivotal aspect to ensure
reaching the intended level of environmental aware-
With the emerging Vehicle-to-everything (V2X)
technology, road users can exchange data, which
opens the horizons for a multitude of applications
(Boban et al., 2018), like Cooperative Awareness
(Sjoberg et al., 2017), Cooperative Maneuver (3GPP,
2016), Teleoperated Driving (3GPP, 2016), and Col-
lective Perception (Shan et al., 2021; Fukatsu and
Sakaguchi, 2019; Pilz et al., 2021; Shan et al., 2021;
Barbieri et al., 2021).
In this work, we focus on some of today’s issues
of Collective Perception (CP) and introduce a possi-
ble solution for enhancing the vehicles’ perception in
specific environments.
Having different perception levels of the sur-
rounding environment, vehicles and other road users
can participate in the collective perception by ex-
changing their detected objects. This helps the road
users to extend their perception range including the
Khalil, A., Meuser, T., Alkhalili, Y., Anta, A., Staecker, L. and Steinmetz, R.
Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems.
DOI: 10.5220/0011065000003191
In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2022), pages 346-352
ISBN: 978-989-758-573-9; ISSN: 2184-495X
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
objects outside their Field of view (FOV) (e.g., ob-
jects hidden by a building). Nevertheless, there is
a wide set of challenges within CP that needs to be
considered. As CP is performed in three main steps,
sensing, communicating, and fusing (Pilz et al., 2021),
challenges can arise in each one of these steps. More-
over, having diverse levels of sensing technologies
on the vehicles, the huge amount of transmitted data
(Fukatsu and Sakaguchi, 2019), and the use of stati-
cally trained onboard Deep Neural Networks (DNNs)
used for objects detection, are open challenges that
have to be tackled by researchers.
In this work, we handle the issue of having on-
board DNNs which are statically trained with context-
agnostic data sets. These DNNs are statically trained
and deployed on the vehicles, which could affect their
detection performance in some certain circumstances
(e.g., fog, heavy rain at night). Moreover, DNNs suf-
fer from the high costs of performance improvement
(NEIL et al., 2021), as the amount of data required
for generalized DNNs scale exponentially with the re-
quired accuracy of these models.
Federated Learning (FL) has recently attracted
considerable interest in the field of vehicular networks
research, and more literature has been published in
recent years showing its ability to train DNNs with a
less amount of transmitted data (Otoum et al., 2020;
Yu et al., 2020; Yuan et al., 2021; Boualouache and
Engel, 2021; Zhao et al., 2021; Ding et al., 2021).
Nonetheless, previous research has tended to focus on
using FL to dynamically train generalized vehicles’
onboard models and failed to make them context-
To alleviate such issues, and inspired by FL con-
cepts, the main contribution of this work is to in-
troduce our forward-looking Situational Collective
Perception (SCP), which enables dynamically-trained
and situation-aware onboard DNNs. We tailor the CP
architecture to enable training the situational DNNs
with context-specific data, and deploy it on the vehi-
cles to enhance their detection capability in specific
The rest of the paper is organized as follows: In
Section 2 we provide more insight into CP, its ele-
ments, key features, and its main challenges. Section
3 presents an overview of FL and highlights the recent
works on applying FL concepts in vehicular networks.
We introduce Situational Collective Perception (SCP)
in Section 4, presenting its potential for enhancing the
detection capability of the vehicles. In this section,
we also highlight the possible future research direc-
tions and challenges of adopting SCP in vehicular net-
works. Finally, in Section 5, we conclude this work.
The term collective perception refers to one of the
emerging technologies in Intelligent Transportation
Systems (ITSs). Figure 1 illustrates the main compo-
nents of collective perception. Each vehicle uses its
onboard sensors (e.g., camera, lidar, radar) to gather
data about the surrounding environment. The gener-
ated raw sensor data is preprocessed (e.g., size re-
duction, fusion) and fed to Deep Neural Networks
(DNNs) for object detection and classification. The
vehicle uses a list of the detected objects and merges
it with their spatial information to build its local envi-
ronment model. This local environment model is the
basis of decision-making and planning systems.
However, the main weakness of this individual
perception (without data exchange between vehi-
cles) is that it highly depends on the onboard sensor
technology (obstruction, detection range, precision),
which restricts the perception range to each vehicle’s
Field of view (FOV). To tackle this issue, and as il-
lustrated in Figure 1, each vehicle can exchange per-
ception data with other vehicles (or road users) to ex-
tend its awareness beyond its limited individual per-
In general, there are two ways to exchange data
between the vehicles (Meuser, 2020): (a) with the
help of base stations (infrastructure-based) which
play the role of data forwarders between the vehi-
cles, (b) without any help of external infrastructure
(infrastructure-less) in which vehicles directly ex-
change data with each other. The appropriate com-
munication type depends on the use case. More-
over, and in some use cases, a hybrid type should
be applied, using both aforementioned communica-
tion types for serving a certain use case. Regardless
of the communication type, each vehicle encapsulates
its perception data in Collective Perception Messages
(CPMs) (ETSI, 2019) and sends them to other vehi-
cles. This perception data comes with different de-
tail levels (Pilz et al., 2021). The receiving vehicle
then fuses the received objects with its local environ-
ment model. This includes different processing tasks,
like coordinate transformation (Shan et al., 2021), and
enriches the vehicle’s perception of the surrounding
environment. This enhanced perception can be ex-
ploited in many other safety and efficiency applica-
tions in vehicular networks.
Although collective perception sounds promising,
and applying it can be essential for enhancing safety
and efficiency, previous works, however, show that it
comes with different kinds of challenges. In the next
section, we introduce some of the main collective per-
ception challenges.
Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems
KOM Multimedia Communications Lab 4
Decisions making and planning systems
Detection and classification
Collective Perception Architecture
Vehicle 1
Data Exchange
Base Station
Sensors’ data
Local environment model
Decisions making and planning systems
Detection and classification
Vehicle 2
Sensors’ data
Local environment model
Figure 1: Illustration of the main components of collective perception. (a) Two vehicles are exchanging perception data with
the help of a base station (infrastructure-based). (b) Two vehicles are directly exchanging perception data (infrastructure-less).
2.1 Collective Perception Challenges
Collective perception is a complex task and comes
with many challenges. While performing collective
perception, issues can occur in its all stages, sens-
ing, communicating, and fusing (Pilz et al., 2021).
This introduces major computational and commu-
nication difficulties. In the following, we will fo-
cus on two main challenges of collective perception:
we start with illustrating the issue of having stati-
cally trained DNNs and making the case for context-
specific DNNs. Then, networking issues that could
occur while exchanging the heavy perception data are
2.1.1 Statically Trained DNNs
One pivotal aspect which affects the vehicle’s scene
understanding capability is how the DNNs used for
object detection perform. The main weakness of
the currently used DNNs comes from the fact that
they are statically trained. Moreover, the data sets
used for the training are commonly context-agnostic,
which means that they are not tailored to specific sit-
uations (context-specific). These situations can be,
e.g., location-specific (geographic), time-specific, or
weather-specific. Using context-agnostic data sets for
statically training the DNNs reduces their detection
ability in certain situations, which may cause safety
disasters (Dickson, 2020). On the other hand, to
improve the performance of the DNNs, a massive
amount of context-agnostic training data should be
gathered, and a long training time is required. The
training time and the amount of data required for the
generalized DNNs scale exponentially with the re-
quired accuracy of these models (NEIL et al., 2021).
Moreover, these DNNs may not reach, in the end,
the intended performance in specific situations. This
highlights the interest of evolving to DNNs that are
dynamically trained and more specific to the differ-
ent kinds of situations. In Sections 3 and 4, we
will describe an approach on training and deploying
situation-specific DNNs by employing the emerging
Federated Learning (FL) concepts in the collective
perception process.
2.1.2 High-volume Data Exchange
In their work, Fukatsu et al. (Fukatsu and Sakaguchi,
2019) measured the data rate required for vehicles
equipped with 3D LiDAR trying to execute collec-
tive perception for overtaking. Their study showed
that even with vehicles’ velocity of 50 km/h, the re-
quired data rate can easily reach around 6 Gbps if
only raw data was exchanged. Considering that this
data rate is generated only by a single perception ap-
plication, thus, running more vehicular applications
which require cooperation and data exchange between
the vehicles will introduce serious challenges for the
vehicular communication network. This emphasizes
the importance of reducing the amount of transmit-
ted data, either by reducing the number of exchanged
messages or by reducing its volume. In Section 4,
we propose Situational Collective Perception (SCP)
to reduce the data exchanged, making it suitable for
improving vehicles’ perception.
In this section, we provide background on the applica-
tion of Federated Learning (FL) approaches in vehic-
ular networks. We employ FL as a basis for our Sit-
uational Collective Perception (SCP) approach. FL
is a promising machine learning model-training ap-
proach which has been introduced (Brendan McMa-
han, 2017) as a solution for privacy issues of the con-
ventional model training approaches (Li et al., 2020).
With FL, instead of collecting the training data from
clients in the cloud, data is kept locally at each client.
A selected set of clients train their models locally with
their local data and then send the updated models (i.e.,
the gradients) back to the server, which aggregates the
model and sends it back to the clients. With its ability
VEHITS 2022 - 8th International Conference on Vehicle Technology and Intelligent Transport Systems
KOM Multimedia Communications Lab 7
Dynamic Model Training Approaches
Initial approach
data before
model after
Huge amount of transmitted data.
Federated learning approach
Sharing the
global model
Less amount of transmitted data (only
gradients and metadata).
Privacy preserved.
(a) Central training
KOM Multimedia Communications Lab 7
Dynamic Model Training Approaches
Initial approach
data before
model after
Huge amount of transmitted data.
Federated learning approach
Sharing the
global model
Less amount of transmitted data (only
gradients and metadata).
Privacy preserved.
(b) FL training
Figure 2: Illustration of two dynamic detection deep neural
networks training approaches, (a) shows the conventional
approach, in which raw data (images, point cloud data) is
transmitted to the central server to be used for training the
DNN. (b) vehicles only transmit their model updates to the
central server to be aggregated.
to work efficiently with Non-Independent and Identi-
cally Distributed (Non-IID) data of a huge number of
clients, FL has attracted the interests of many indus-
trial fields. Recently, FL has received considerable
attention in vehicular networks research (Du et al.,
2020; Elbir et al., 2020). More literature is getting
published in recent years highlighting the privacy-
preserving features of applying FL to vehicular net-
works (Otoum et al., 2020; Yu et al., 2020; Yuan et al.,
2021; Boualouache and Engel, 2021; Zhao et al.,
2021; Ding et al., 2021). In (Vyas et al., 2020), Vyas
et al. proposed an architecture based on FL for de-
veloping a model that predicts the driver stress level,
and then use these predictions as a basis for a driver
recommendation system.
Furthermore, FL facilitates obtaining
dynamically-trained onboard Deep Neural Net-
works (DNNs) with a reduced volume of exchanged
data. So far, there are two approaches to obtain
dynamically-trained DNNs, to improve its perfor-
mance over time. Figure 2 illustrates those two
approaches. In the conventional DNNs training
approach (Figure 2a), vehicles transmit the raw data
(images, point cloud data, etc.) to a central server.
The central server uses the gathered data to train
the global model and transmits it to the vehicles
after validating it. This approach induces heavy data
volume transmission, which can lead to network
congestion issues. Moreover, the transmitted data can
contain sensitive information which can be leaked
during transmission or while processing it at the
central server (Nasr et al., 2019), which leads to
privacy issues. On the other hand, and as depicted
in Figure 2b, the FL DNN training approach has the
potential to address such networking and privacy
issues. Instead of transmitting heavy volume and
sensitive data to the central server, a selected number
of vehicles can participate in the training process
without transmitting their local data to the server. The
central server collects vehicles’ model updates and
aggregates them using common averaging algorithms
like federated averaging (Kone
y et al., 2016).
After that, the vehicles receive the global model,
which replaces their local models (DNNs).
This approach enables maintaining dynamically-
trained DNNs, taking into consideration the commu-
nication network limitations as well as the privacy
concerns (Barbieri et al., 2021; Nguyen et al., 2021).
Nevertheless, the existing literature using FL in ve-
hicular applications focus on generating DNNs that
are trained with context-agnostic data. Making these
DNNs prone to fail while detecting objects in certain
situations as mentioned in Section 1. A reasonable
approach to tackle this issue could be to introduce
situation-specific DNNs which can be used by the ve-
hicles in specific situations.
In Section 4, we tailor the collective perception ar-
chitecture in a FL way to enable dynamically trained,
and situational DNNs which lead to adaptive, and ef-
ficient collective perception.
In this section, we provide a comprehensive expla-
nation of our forward-looking Situational Collective
Perception (SCP) architecture as a potential solu-
tion for the issues of having onboard Deep Neural
Networks (DNNs) which are statically trained with
context-agnostic data.
In the following, we introduce the SCP roles, then
explain the models exchanging workflow between
these different roles. Next, we highlight some of
the potential research directions and challenges in the
area of SCP.
4.1 SCP Roles
Our proposed SCP design consists of three compo-
nents (see Figure 3):
Vehicles or Other Entities Participating in Col-
lective Perception (CP): each vehicle communi-
cates with appropriate edge servers to retrieve the
suitable situational models, after detecting a situa-
tion change. With the increased number of vehic-
ular applications, car manufacturers are continu-
ously improving the cars’ computational power.
Thus, we assume that the vehicles have the re-
quired computational power for training and vali-
dating the received situational models. Moreover,
we assume for simplicity that all vehicles have
the same sensor setup, so that they can work with
copies of the same situational DNNs.
Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems
KOM Multimedia Communications Lab 12
Detection and classification
Research Challenges/Approaches
Situational models
Edge server
Edge server
Edge server
Cloud server
Decisions making and planning systems
Sensors’ data
Local environment model
Figure 3: Illustration of three research directions in the situational collective perception.
Edge Server: unlike a simple base station illus-
trated in Figure 1 which only plays the role of data
forwarder between the vehicles, we assume that
the edge server has the required computation and
communication power to maintain its responsibil-
ities. The edge server is an essential part of the
SCP architecture, as it is responsible for manag-
ing the different situational models, and sending
the proper one to the legitimate vehicles. Besides
communicating with vehicles, the edge server has
to communicate with other edge servers and the
cloud server in order to exchange and validate the
different situational models.
Cloud Server: the main task of the cloud server
is to orchestrate the overall SCP processes. The
cloud server is responsible for initializing the sit-
uational models and transferring them to the edge
servers. Having a wide set of different situa-
tional models spread across the edge servers, the
cloud server communicates with all edge servers
to maximize the benefits by combining the learn-
ing parameters contained in the situational mod-
4.2 SCP Workflow
To express the validity of the situational models, we
introduce two different models’ flags: The training
flag indicates that the current version of the model is
non-valid and vehicles can not rely on it for detec-
tion purposes. Thus, vehicles have to train the model
with their local data, to eventually make it valid. On
the other hand, the valid flag indicates that the cur-
rent version of the model is valid and thus, vehicles
can use it for detection purposes and train it simulta-
The overall process starts when the cloud server
uses a pre-defined set of situations to initiate the sit-
uational models. These models then are trained ini-
tially by the cloud server with the available context-
specific data sets. If no context-specific data set is
found for a situational model, the cloud server skips
the initial training process for that model. Later, the
cloud server transmits the situational models to the
edge servers, after marking the models with training
flags, as they are not ready yet to be used for detection
Model training ||
Model training
and using
Other edge servers
Cloud server
“training || valid”
Edge Server
Model merging
with local model
Model marking
with “valid” flag
Merged model
Situational model
Model marking
with “training”
Figure 4: Illustration of situational models exchanging
After that, and as depicted in Figure 4, when
the vehicle detects a new situation, it sends a situa-
tional model request to the edge server. The Edge
server checks the availability of the requested situ-
ational model. If the situational model is available,
the edge server transmits it to the requesting vehi-
cle. Upon receiving the situational model, the vehi-
cle checks the model’s flag. If the model is training
flagged, the vehicle only trains it with its locally gath-
ered data. On the other hand, if the vehicle receives a
valid model, it can use it for detection purposes while
training it simultaneously. As soon as the vehicle de-
tects the situation change, it re-transmits the model
back to the edge server for validation. When receiving
a model from the vehicle, the edge server merges it
VEHITS 2022 - 8th International Conference on Vehicle Technology and Intelligent Transport Systems
with its local situational model and validates the pro-
duced model. If the validation for produced model
fails (e.g., the new version is less performing than the
previous one and it does not reach a specific perfor-
mance threshold), the edge server marks it with the
training flag. On the other hand, if a valid model is
obtained, the edge server marks it with the valid flag.
Finally, the edge server replaces the old version of the
model with the newly produced version.
Although this workflow sounds feasible, one
question that needs to be asked, however, is how the
vehicles will be able to annotate their local data. In
the next section 4.3, we will provide one possible
technique that enables the vehicles to annotate their
local data before using it for training the situational
4.3 SCP Research Directions and
Figure 3 reveals three possible research areas related
to SCP. Each research area is associated with a set of
challenges that needs to be addressed in order to ob-
tain benefits from employing SCP in real-world appli-
We organize SCP future research directions under
three headings:
1. Utilize Situational Collective Perception to En-
able Situational Models Exchange: since the
idea of using situation-specific models is not in-
troduced in the past in the vehicular applications
area, it is important to evaluate its applicability in
real-world scenarios. Moreover, during the mod-
els exchanging process (see Figure 4), the vehicles
have to annotate their locally gathered data before
using it for training the situational models. This
is considered to be one of the main challenges
in Federated Learning (FL). A possible way to
handle this challenge is by using Active Learning
(AL) approach (Ahmed et al., 2020). With AL,
vehicles can use pre-trained models for selecting
and annotating the training data with an expected
level of uncertainty. This annotated data can be
used then to train the situational models. More-
over, comprehensive studies are required to eval-
uate if the SCP approach outperforms in specific
situations the conventional approach of having
statically trained models using context-agnostic
data. In addition, the performance of SCP relies
mainly on the number of participating vehicles.
Thus, enabling efficient SCP in rural areas could
be a difficult task.
2. Situational Models’ Validation: it is important
to bear in mind that vehicular applications are
very safety and security-sensitive. Intensive val-
idation mechanisms need to be developed to en-
sure the validity of the different situational mod-
els. Validation is not only required from the edge
server-side but also the vehicles must validate the
received models. Vehicles may require to perform
a validity check on the valid models received be-
fore relying on them for detection. On the other
hand, edge servers are required to validate the
situational models continuously, with the help of
other edge servers and the cloud server. It is cru-
cial though to develop validation criteria, defining
the most important parameters which make the
model either marked with valid or training flag.
Another possible area of future research would be
to investigate on how to ensure that SCP architec-
ture is resistant against the anomalous nodes (ve-
hicles, or edge servers).
3. Situational Collective Perception at Large
Scale: perhaps one of the most powerful fea-
tures of SCP comes from having a huge collec-
tion of multiple situational models spread across
a wide area. However, in order to maximize
the benefits, future research has to develop inten-
sive mechanisms to deal with the heterogeneity at
a large scale (different models for diverse situa-
tions). Another possible area of future research
would be to investigate on how to manage the sit-
uational models’ exchange between the different
entities, to ensure robustness.
To detect their surroundings, vehicles employ stati-
cally trained onboard DNNs. These DNNs are prone
to detection failures in some situations, as they are
commonly trained with context-agnostic data. In
this work, we introduced the forward-looking SCP to
tackle these issues. Inspired by the FL approach, we
tailor the collective perception architecture to enable
dynamically trained and situation-specific DNNs. We
aim to facilitate enabling adaptive, and efficient col-
lective perception in future vehicular networks. We
highlighted three possible SCP research areas and em-
phasized some of its expected challenges.
This work has been funded by the German Research
Foundation (DFG) within the Collaborative Research
Center (CRC) 1053 MAKI.
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