Enhancing Vulnerable Road User Awareness of Intelligent Transport
Systems Through Relay and Aggregation of Collective Perception
Messages with Road Side Units
Vincent Albert Wolff
a
Institute of Communication Technologies, Leibniz University Hannover, Appelstr. 9a, Hannover, Germany
Keywords:
Collective Perception, Collaborative Driving, V2X, V2I, Road Side Unit, Vulnerable Road User Awareness,
Vehicular Adhoc Networks, Road Safety.
Abstract:
The Collective Perception Service (CPS) allows connected vehicles to gain a more comprehensive picture of
their environment by sharing information about the dynamic state of objects with other vehicles and infras-
tructure. Objects detected by on-board sensors are shared through Vehicle-to-Vehicle (V2V) or Vehicle-to-
Infrastructure (V2I) communication. However, the range of V2V communication is limited, and Road Side
Units (RSUs) can be deployed to enhance the range and attenuate the negative effects of V2V signal propaga-
tion. We enhance the vehicular network by RSUs to aggregate and forward Collective Perception Messages
(CPMs) received from neighboring vehicles, thus improving the overall environmental perception and the per-
ception of Vulnerable Road Users (VRUs) in particular. Our simulation results, based on the ETSI ITS-G5
standard, demonstrate the effectiveness of the CPS in an urban intersection scenario, showing the positive
impact of additional V2I communication and the deployment of RSUs on vehicular perception of VRUs. The
addition of RSUs results in a significant improvement in VRU perception, while packet loss on the network
channel increases moderately.
1 INTRODUCTION
Recent advancements in Advanced driver-assistance
systems (ADAS) and autonomous driving have led to
the development of a variety of services for Intelligent
Transportation System Stations (ITS-Ss), including
collision detection, emergency braking, lane-keeping
assistance, and many others. These services require
accurate and reliable information from ITS-Ss, which
must provide high-precision local sensor data and
a comprehensive Local Environment Model (LEM).
The LEM of each vehicle is updated by the on-board
sensor detections, as modern ITS-Ss are equipped
with Lidar, radar or camera sensors. Vehicle-to-
Everything (V2X) communication provides services
to enhance the LEM by receiving messages from
other stations about the position and dynamics of
one vehicle (Cooperative Awareness Message, CAM)
and by sharing the LEM with other ITS-Ss. This is
provided by the Collective Perception Service (CPS)
proposed by the European Telecommunications Stan-
dards Institute (ETSI).
a
https://orcid.org/0000-0003-4210-3035
The Collective Perception Service does not need
to rely on Vehicle-to-Vehicle (V2V) links exclusively.
The V2V communication can be assisted by Vehicle-
to-Infrastructure communication, which enables mes-
sage exchange between Road Side Units (RSUs) and
connected vehicles. The CPS has lots of potential to
decrease the risk of collisions, as it provides ITS-S
with data about other traffic participants, objects and
obstacles in its surrounding. Especially Vulnerable
Road Users (VRUs), i.e. pedestrians and cyclists,
are more prone to critical accidents with high fatal-
ity rate (den Berghe, 2021). VRUs are not equipped
with sensors and have no communication capabili-
ties, making them all the more reliant on the V2X
communication, in particular Collective Perception.
The ETSI CPS (ETSI, 2019b) tackles this commu-
nication gap and provides the ITS-S with informa-
tion about current VRU dynamics and position which
is gathered by the on-board sensors of each station.
In current research the CPS is mostly investigated in
terms of its capabilities to improve the environmen-
tal awareness ratio (EAR) of vehicles and safety re-
lated metrics (Schiegg et al., 2021). While Collective
Perception has been shown to improve vehicle detec-
Wolff, V.
Enhancing Vulnerable Road User Awareness of Intelligent Transport Systems Through Relay and Aggregation of Collective Perception Messages with Road Side Units.
DOI: 10.5220/0011973300003479
In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023), pages 337-343
ISBN: 978-989-758-652-1; ISSN: 2184-495X
Copyright
c
2023 by SCITEPRESS Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
337
tion and increase safety by reducing blind spots, re-
lated work often overlooks the perception of VRUs.
In this work the focus is on enhancing the percep-
tion of VRUs, which is crucial for ensuring road traf-
fic safety. As VRU safety is most critical in urban
environments, a scenario for an urban intersection is
created. In addition, the impact of RSUs is exam-
ined by placing an RSU at the intersection center.
The RSU helps to compensate urban-specific commu-
nication challenges such as multi-path signal prop-
agation, diffraction and shadowing due to building
and other obstacles. Maintaining a reliable commu-
nication link even with Non-Line-Of-Sight (NLOS)
communication has big impact on the CPS: Research
found that NLOS links significantly reduce communi-
cation range and signal strength not only implement-
ing 802.11p on physical layer, but also for the upcom-
ing 802.11bd and 5G NR-V2X. (Anwar et al., 2019).
In this study, we simulate the Cooperative Per-
ception System (CPS) using the ETSI ITS-G5 (ETSI,
2020) and CPM (ETSI, 2019b) standards in an urban
setting. The simulated scenario incorporates typical
signal propagation characteristics at intersections and
varying densities of vehicles and VRUs. The service
performance is examined by the vehicular environ-
mental awareness of VRUs and network related met-
rics.
2 RELATED WORK
2.1 ETSI Collective Perception
The ETSI standardizes CPM and proposes generation
rules which define which objects and with which fre-
quency these objects will be added to a CPM. The
dynamic-based generation follow these rules:
1. The Object is detected for the first time
2. Object has moved more than 4 meters since last
inclusion
3. Object velocity changed more than 0.5m/s since
last inclusion
4. Object heading angle has changed more than 4 de-
grees since last inclusion
5. Object is a VRU and was not included in a mes-
sage in the last 500ms
After the generation rules are applied, redundancy
mitigation techniques can be deployed to filter re-
dundant objects. There are frequency-based, entropy-
based, dynamic-based and confidence-based rules de-
fined, which combine object information of local de-
tection and received V2X-detection to determine the
priority to send one object (ETSI, 2019b). Redun-
dancy mitigation rules were studied by (Delooz et al.,
2022) in terms of environmental awareness, channel
load and information redundancy. They found that no
single redundancy mitigation technique reaches all re-
quirements and propose a filtering algorithm to com-
bine different mitigation techniques.
2.2 Infrastructure-Assisted CPM
To enhance object detection reliability and range,
RSUs can be integrated in an VANet to assist the V2V
links. (Yu et al., 2022) provide an overview of related
studies on infrastructure-assisted vehicular services.
They focus on object perception and detection using
different type of on-board sensors (camera, Lidar and
a fused system).
There are two different operating options of RSUs
related to the CPS: RSUs can either detect objects
with local sensors and create CPMs from local detec-
tions or they relay received CPMs by other ITS-Ss.
The impact of RSUs on Collective Perception has
been studied in several forms: (Chtourou et al., 2021)
studied a RSU implementation with 802.11p in the
VEINS simulator. They conclude that RSU-assisted
CPS provides higher efficiency compared to CPS
based on V2V communication. (Pacella et al., 2021)
used a pure Road Side Unit Scenario, where the RSU
is equipped with sensors to broadcast CPMs. Their
focus is on latency measurements, where they esti-
mate an end-to-end delay of less than 250ms. (Gar-
lichs et al., 2020) implemented a relay mechanism to
aggregate CAMs received by vehicles and broadcast
the aggregated information in CPMs. They studied
that the driver reaction time can be improved while
the additional network load remains moderate.
2.3 Vulnerable Road User Safety
The safety of Vulnerable Road Users (VRUs), such
as pedestrians and cyclists, plays a critical role in the
concept of Collective Perception within transporta-
tion systems. As these individuals are typically not
equipped with Vehicle-to-Everything (V2X) commu-
nication modules, they must rely on vehicular safety
systems for protection. One approach to address this
issue is to integrate VRUs into a Vehicular Ad-hoc
Network (VANet) by allowing them to broadcast their
own messages regarding their location and move-
ments. The European Telecommunications Standards
Institute (ETSI) has standardized the Vulnerable Road
User Awareness Message (VAM) to facilitate commu-
nication between VRUs and vehicles (ETSI, 2019a).
However, it is important to note that the widespread
VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems
338
<PerceivedObjectContainer>
Object #1
Object #2
Object #3
CPM
...
<PerceivedObjectContainer>
Object #1
Object #4
Object #5
CPM
...
<PerceivedObjectContainer>
Object #2
Object #6
CPM
...
<PerceivedObjectContainer>
CPM
...
Object #1
Object #2
Object #3
Object #4
Object #5
Object #6
RSU
Figure 1: Aggregation of CPMs in the RSU.
adoption of this technology cannot be guaranteed, and
the accuracy and reliability of VRU communication
devices, such as smartphones, must be established be-
fore they can be effectively utilized.
(Willecke et al., 2021) simulate ETSI Collective
Perception using the Artery framework and show that
the CPS can significantly improve vehicular aware-
ness of VRUs while maintaining a stable network
load. Also alternative parameterizations for CPS are
analyzed to indicate different tracking accuracies for
person objects. (Lobo et al., 2022) study VAM and
a combination of CPM and VAM, which outperforms
implementing one standalone service.
3 CONTRIBUTION
Although urban scenarios have already been studied
in regards to CPM, there is a lack in research of the
combination of VRUs and RSUs in characteristic ur-
ban environments. As current research of RSUs sug-
gest a placement at intersections centers (Wang et al.,
2017), it is worth to study the impact of RSUs on
VRU awareness. As stated in 2.2 related work found
that the relay mechanism can be beneficial imple-
menting the aggregation of Cooperative Awareness
(CA) messages, which indicates that the relay and ag-
gregation of CPMs can be a next step to be deployed.
Our goal is to demonstrate the impact of a re-
lay and aggregation system on the perception ratio of
VRUs. Most related work focuses on general object
perception and seldom takes into account the unique
safety concerns of VRUs. Compared to ITS-equipped
vehicles, VRUs lack the ability to broadcast their
location and current movement via the CA service,
which makes them more dependent on the CPS.
We also investigate the impact of the additional
RSU message dissemination on the overall network
channel load, as the CPS bandwidth is limited and
competes with other V2X technologies. In addi-
tion, different vehicle densities and market penetra-
tion rates are tested to analyze the impact of increas-
ing channel congestion. The simulation is conducted
using the ETSI ITS-G5 communication model and
ETSI CPM generation rules according to 2.1, provid-
ing a realistic communication environment and ensur-
ing comparability with other studies.
This simulation study examines the combination
of V2I and V2V links in order to fully evaluate the
impact of Road Side Units (RSUs) on the perception
of the environment. To provide a comprehensive anal-
ysis, we compare the results of the implementation
with both V2I and V2V links enabled to those ob-
tained using pure V2V communication without RSUs.
4 IMPLEMENTATION
The proposed implementation utilizes an RSU relay
and aggregation mechanism, which is described in
the following section. The urban scenario poses sig-
nificant challenges for signal propagation, hence the
GemV2 model (Boban et al., 2014), which supports
different types of signal links, is used. Additionally,
the characteristics of the chosen road scenario and
traffic simulation will be discussed.
4.1 CPM Aggregation and Relay
Each car generates its own CPMs based on dynamic
ETSI rules described in 2.1, which are broadcasted
to ITS-Ss in communication range. When the CPMs
are received by the RSU, they are aggregated to build
newly generated CPMs, ideally containing a greater
number of objects in the PerceivedOb jectContainer.
The RSU relays the messages with a fixed frequency
of 100ms. Figure 1 shows an example of the aggrega-
tion, where the RSU receives CPMs by 3 cars and ag-
gregates the including objects to one new CPM. The
LEM of the RSU is created out of the LEMs of the
vehicles in the VaNET according to equation 1.
LEM
RSU
=
n
i=1
LEM
Vehicle(i)
(1)
In our RSU service implementation, VRUs are
given the highest priority when generating and aggre-
gating CPMs. After the type of object, the next pri-
Enhancing Vulnerable Road User Awareness of Intelligent Transport Systems Through Relay and Aggregation of Collective Perception
Messages with Road Side Units
339
ority is the age of the information. This ensures that
the most recent sensor measurements are prioritized
when generating and aggregating CPMs. The infor-
mation shared among connected vehicles is as up-to-
date as possible.
The use of Road Side Units (RSUs) in this imple-
mentation allows for enhanced range and improved
signal propagation of the CPMs, which can improve
the overall perception and safety of connected vehi-
cles in urban environments.
4.2 Signal Propagation Modelling
Simulating V2X communication requires a signal
propagation model to implement realistic communi-
cation characteristics. For the simulation we choose
the GemV2 geometry-based model for simulating
Vehicle-To-Everything (V2X) communication. It
considers three types of links: line-of-sight (LOS)
links, non-line-of-sight (NLOS) links due to vehicles,
and NLOS links due to static objects.
In GemV2, the LOS links are modeled using a
two-ray propagation model. This model considers the
direct path between the transmitter and receiver, as
well as a reflected path off of a reflecting object, such
as a building or vehicle. The reflected path can affect
the signal strength and the performance of the V2V
communication system.
Furthermore GemV2 considers NLOS links due to
vehicles and static objects. In these cases, the signal
must pass through or around the obstructing objects,
which can cause attenuation, delay, and other impair-
ments. GemV2 takes into account the geometry of the
environment and the objects within it to accurately
model the NLOS links and their effects on the V2V
communication system. (Boban et al., 2014)
Equation 1 defines the well-known two-ray
ground reflection model, which models the V2X sig-
nal propagation. (Sommer and Dressler, 2011)
|E
TOT
| =
E
0
d
0
d
LOS
cos(ω
e
(t
d
LOS
c
))
+R
ground
E
0
d
0
d
ground
cos(ω
c
(t
d
ground
c
))
(1)
For the pass loss PL, the following equation ap-
plies:
PL(d) = PL(d
0
) + 10 γ log
10
(
d
d
0
) (2)
The factor γ is set differently for LOS, NLOSb
(building) and NLOSv (vehicle) links to model the
signal propagation according to different types of ob-
stacles. GemV2 has the ability to efficiently scale to
Figure 2: SUMO scenario of the simulation.
high-density scenarios, where there are many vehicles
and obstacles that can affect the V2V communication.
GemV2 uses geometric calculations and approxima-
tions to quickly and accurately simulate V2V commu-
nication in these scenarios.
5 SIMULATION
The simulation is built to evaluate the performance
of a vehicle-to-everything (V2X) communication sys-
tem in accordance with the ETSI ITS-G5 (ETSI,
2020) standard. The simulation environment is con-
structed using the open-source framework Artery
(Riebl et al., 2015). The traffic simulation is imple-
mented in a scenario created using the Simulation of
Urban Mobility (SUMO) (Krajzewicz et al., 2002).
A two-lane road in each direction is simulated, with
an intersection arm length of 150 meters. Two dif-
ferent density modes are evaluated, one with a den-
Table 1: Simulation parameters.
Parameter Value
Enabled services ETSI CA, ETSI CPS
Physical layer IEEE 802.11p
Bit rate 6Mbit/s
Carrier frequency 5.9GHz
Bandwidth 10MHz
Channel G5-CCH
Transmission power 200 mW
Signal threshold -85 dBm
Noise threshold -65 dBm
V2X propagation model GEMV2
Penetration rate 100%
DCC Disabled
RSU max antenna gain 10dB
RSU antenna type Constant gain antenna
Vehicle sensor set 85m 360°camera
VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems
340
Figure 3: EAR of VRUs for low density scenario
sity of 75 vehicles per kilometer and the other with a
density of 150 vehicles per kilometer. The number of
VRUs is set proportional to the vehicular density. The
VRUs consist of cyclists and pedestrians, that share
the sidewalk. The scenario implemented in SUMO
is illustrated in Figure 2. Obstacles (grey areas) are
placed on each intersection arm to simulate the im-
pact of buildings at intersections. The evaluation is
conducted at the intersection center, and the impact of
network load and sensor detections by the outer junc-
tions is mitigated by filtering the data to only consider
vehicles in the area of interest. The RSU is placed at
the intersection center.
Simulation parameters are set according to table
1. The physical layer of the system is based on the
IEEE 802.11p standard, which specifies the use of
the 5.9 GHz frequency band for wireless communi-
cation in intelligent transportation systems. The chan-
nel used in the simulation has a bandwidth of 10MHz.
The transmission power is set to 200 mW. The signal
and noise thresholds are -85 dBm and -65 dBm, re-
spectively, which represent the reconstruction thresh-
olds of the signal from noise. The V2X propagation
model used in this simulation is GemV2, described
in 4.2. The penetration rate, which is the proportion
of vehicles equipped with V2X technology, is set to
a range of values: 5%, 10%, 25%, 50%, and 100%.
The Decentralized Congestion Control (DCC) is dis-
abled, which means that the system does not control
the flow of data on Facilities or Access layer. The
type of antenna used is a constant gain antenna (omni-
directional), thus the gain of the antenna does not vary
with frequency or direction. In the simulation ve-
hicles are equipped with 360-degree camera sensors
with a range of 85m as on-board sensors, while the
RSU does not have any environmental sensors.
As ITS-Ss can be equipped with multiple services,
besides the CPS the CA service is enabled.
Figure 4: EAR of VRUs for high density scenario.
6 RESULTS
For the evaluation of our simulation, perception and
network related metrics are investigated. We adapt the
Environmental Awareness Ratio (EAR) metric from
(Schiegg et al., 2019), which was originally intro-
duced for objects of any type. Figure 3 and 4 shows
the EAR of the vehicles in respect to VRUs accord-
ing to equation 3. It is defined as the ration between
the sum of detected VRUs within a specific range and
the total number of VRUs in the range. The range of
interest is set to 200m in the simulation. The tracking
age of information is set to a maximum of 1 second.
Tracks with older updates are not considered.
EAR(V RU s) =
n
i=1
V RU
Detected,i
n
i=1
V RU
Range,i
(3)
Figure 3 and 4 shows the EAR for low- and high-
density scenarios, respectively. In both cases, it is
clearly demonstrated that the EAR increases as the
penetration rate of equipped vehicles increases. This
outcome is expected as the number of vehicles capa-
ble of sharing locally-sensed objects increases. The
impact of the RSU on the EAR is significant. For ex-
ample, with no RSU and a 5% penetration rate, the
mean EAR jumps from 0.26 to 0.62. This trend is
consistent for higher penetration rates, though the rel-
ative increase becomes smaller. In the low-density
scenario, a median of 100% EAR is achieved at a pen-
etration rate of 50%. In contrast, for the high-density
scenario, the median is only reached at a penetration
rate of 100% with the RSU enabled. Another impor-
tant observation is that when the RSU is turned on, the
lower quartile value is significantly improved, provid-
ing a higher minimum coverage of detected objects.
It is also noteworthy that the overall EAR is lower for
the high-density scenario. This could be due to the
fact that while one vehicle may detect more VRUs
Enhancing Vulnerable Road User Awareness of Intelligent Transport Systems Through Relay and Aggregation of Collective Perception
Messages with Road Side Units
341
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
PDR
0.0
0.2
0.4
0.6
0.8
1.0
CDF
Penetration Rate
25 %, No RSU
25%, RSU
100%, No RSU
100 %, RSU
Figure 5: Cumulative Distribution Function of PDR for low
and high penetration rate, RSU turned on and off.
in the high-density case, the total number of VRUs
is also increased. Another possible reason is that with
an increasing number of objects, the obstruction of the
field of view sensor increases. Other reasons could be
network-related and warrant further investigation.
It is observed that the VRU awareness does not
reach 100% without the RSU enabled. At first glance
this is not a result to be expected as previous stud-
ies found that the EAR enhanced by the CPS covers
100% of detected objects even with penetration rates
of 25% and beyond (Schiegg et al., 2021). However
this work focus on the perception of VRUs. Com-
pared to other type of traffic participants, they are of-
ten hidden by the vehicular on-board sensors due to
their small surface. Furthermore, no difference has
been made between VRUs on the sidewalk or VRUs
crossing the street. Therefore a number of undetected
VRUs could be on the sidewalk. However it is worth
to consider all VRUs in the EAR: Pedestrians or cy-
clists could intend to cross the street or behave unex-
pectedly. Another aspect that decreases the EAR is
the inclusion of obstacles in the simulation by plac-
ing buildings at all intersection arms: The on-board
360 degree sensor can be blocked and the commu-
nication range is attenuated due to the NLOS signal
propagation. The evaluation of network-related pa-
rameters is illustrated in figure 5, which shows the
Packet Delivery Ratio (PDR) for a low (25%) and
high (100%) penetration rate, represented as a Cumu-
lative Distribution Function (CDF). In the graph, the
vehicle density is fixed at the low density scenario to
simplify the analysis by reducing the number of pa-
rameters. This type of graph allows for a clear vi-
sualization of the distribution of PDR and the mini-
mum PDR (CDF(x) 0). The graph compares the
PDR with and without the enabled RSU. It can be ob-
served that, for the same penetration rate, there is a
significant increase in network load with the RSU en-
abled, resulting in a lower PDR: The minimum PDR
decreases from 0.75 to 0.39 (25% penetration rate)
and from 0.57 to 0.29 (100% penetration rate), re-
spectively. The 50th percentile drops from 0.89 (25%
penetration rate) to 0.56 and from 0.76 to 0.42 (100%
penetration rate), respectively. Additionally, it is evi-
dent that the variance of the distribution increases as
the PDR decreases, indicating a less reliable system
overall. The observations of PDR also provide insight
into another potential reason for the decreased EAR
in the high-density scenario. As the PDR decreases,
the communication channel becomes increasingly sat-
urated, resulting in a greater number of packet losses.
In an over-congested channel, not all information will
be shared between the ITS-Ss, leading to a reduction
in the EAR.
7 CONCLUSION
The results of the simulation according to ETSI stan-
dards show that an RSU at urban intersections can im-
prove the vehicular perception of VRUs by enabling
the aggregation and dissemination of CPMs to ITS-
Ss. However, it should be noted that this implemen-
tation also incurs a cost in the form of increased net-
work congestion, leading to a decrease in the Packet
Delivery Ratio (PDR) on the communication channel.
Despite this decrease in PDR, the results indicate that
the overall improvement in Environmental Awareness
Ratio (EAR) when the RSU is activated outweighs
the decrease in PDR, resulting in a net benefit from
the utilization of the RSU. It is also revealed that im-
provement of VRU awareness is a challenging task in
highly-congested urban scenarios: High CPS penetra-
tion rates are needed to achieve proper EAR. As the
results are promising, in future work additional sce-
narios can be investigated. Special RSU CPM gen-
eration rules could be introduced to enhance the net
benefit of an RSU at urban intersections, leading to a
more controlled dissemination strategy by the RSU.
This could result in an decrease of network channel
load while maintaining a high EAR.
ACKNOWLEDGEMENTS
This publication is funded by the Lower Saxony
Ministry of Science and Culture under grant num-
ber ZN3493 within the Lower Saxony “Vorab“ of the
Volkswagen Foundation and supported by the Center
for Digital Innovations (ZDIN).
VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems
342
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