Investigating the Safety Effects of Degraded Wireless Performance on

Connected Longitudinal Driver Assistance Functions

Roland Nagy

1,2 a

, Zsombor Peth

1 b

, Tam

as M

arton Kaz

1 c

, Tibor Tur

oczi

and

Arp

ad T

1 d

Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering,

Budapest University of Technology and Economics, M

uegyetem rkp. 3., H-1111 Budapest, Hungary

Jaguar Land Rover Hungary Ltd., Hungary

Keywords:

SNR, V2X, CACC, Safety Effect, Wireless Performance.

Abstract:

In the near future, Vehicle-to-Everything (V2X) based technologies will enable vehicles and other road users

to exchange information with each other, even in cases where the applicability of other sensors is limited. This

technology will be able to ensure the operation of advanced driver assistance systems, in cases where the other

sensors are malfunctioning. In such situations, where only wireless communication can be relied upon, it is

essential to be able to react to possible changes in network performance metrics. The objective of this paper

is to address the aforementioned problem by characterizing the safety-risk associated with vehicle dynamic

parameters and the factors inﬂuencing the network performance metrics in different scenarios. The network

can be divided into seven distinct layers that are responsible for data transmission, and the research primarily

focuses on the physical layer, with the objective of studying its impact on the packets sent. In the research,

the Signal-to-Noise Ratio (SNR) is considered to be the primary network inﬂuencing parameter. This will

facilitate the enhancement of not only the safety of transportation but also its reliability.

1 INTRODUCTION

In the near future, new technologies and develop-

ments will emerge in the automotive and transport in-

dustries to make them perform better and more efﬁ-

cient. One of these technologies will be wireless com-

munication between vehicles and other road partici-

pants (Llatser et al., 2019). With this improvement,

vehicles will be able to exchange the necessary dy-

namic and static information with each other to in-

crease the safety awareness of a vehicle. Unlike other

environmental perception sensors (camera, radar) ve-

hicles equipped with V2X technology can detect ve-

hicles from all directions and use their data to create

a local dynamic map (LDM) to improve ad hoc deci-

sion making to mitigate safety risks and avoid trafﬁc

accidents. To provide real-time wireless communica-

tion the IEEE 802.11p standard (IEEE, 2012) was in-

troduced to support safety-critical application such as

forward collision warning (FCW), cooperative adap-

https://orcid.org/0009-0003-8623-275X

https://orcid.org/0000-0003-3054-4669

https://orcid.org/0009-0002-3247-2646

https://orcid.org/0000-0002-1985-4095

tive cruise control (CACC) and blind spot warning

(BSW) (Mannoni et al., 2019). Building on its foun-

dation, the emerging IEEE 802.11bd standard (Xue

et al., 2024) aims to enhance performance by offer-

ing higher data rates, improved reliability, and back-

ward compatibility, making it a promising solution for

next-generation vehicular communication systems.

Vehicle-to-vehicle (V2V)-based safety commu-

nication can effectively support non-line-of-sight

(NLoS) scenarios by using relay techniques and

multi-hop communication to bypass obstacles and

maintain connectivity between vehicles. Although

there are some major limitations of wireless transmis-

sion, such as signal attenuation (fading, path loss (Bae

et al., 2020)) due to obstructed visibility, interference

from other wireless devices or environmental factors

and these have a direct impact on Quality of Service

(QoS).

The degraded QoS in V2V communications di-

rectly affects the reliability and efﬁciency of data

exchange between vehicles, inﬂuencing by factors

such as transmission latency and packet loss. Inad-

equate Packet Delivery Ratio (PDR) can compromise

the availability of critical safety information, poten-

tially increasing the risk of accidents and reducing the

278

Nagy, R., Pethõ, Z., Kazár, T. M., Turóczi, T. and Török, Á.

Investigating the Safety Effects of Degraded Wireless Performance on Connected Longitudinal Driver Assistance Functions.

DOI: 10.5220/0013088400003941

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th Inter national Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2025), pages 278-285

ISBN: 978-989-758-745-0; ISSN: 2184-495X

effectiveness of advanced driver assistance systems

(ADAS) and automated driving functions (ADF) (Cui

et al., 2018).

The validation of V2X communication-based ap-

plications necessitates the creation of a substantial

number of test and evaluation scenarios. This can be

achieved through a combination of analytical, simu-

lation, and prototyping techniques.

In order to develop a functional simulation envi-

ronment and a reliable and robust feature, it is essen-

tial to conduct a comprehensive review of the exist-

ing literature on this topic. In the study conducted

by (Saponara and Gagliardi, 2018) a simulation envi-

ronment was created in Simulink where they are able

to test the reliability of V2X communication. Further-

more, they compared the simulation results with ex-

perimental results. The proposed tool may prove use-

ful in studying the impact of vehicle distance, speed

and operating scenario on the reliability of the com-

munication system. In their article, (Xing et al., 2019)

proposed a method where the CACC model considers

communication delay and is able to reduce the mini-

mum string-stable time gap. This was achieved by a

Smith predictor and introducing a master-slave archi-

tecture. The results were validated by simulation, but

other factors such as packet reception were not taken

into account.

In their study, (de Almeida et al., 2022) conducted

a comparative analysis of the results obtained from

the utilisation of commercial On-Board Units (OBUs)

and Road-Side Units (RSUs) data, with those de-

rived from the application of diverse simulation soft-

ware. The authors concentrated on three key metrics:

maximum communication range, packet delivery ra-

tio and inter-reception time. These were evaluated

while varying the vehicle speeds within an 802.11p

domain. However, in this study, the effect of noise on

the communication network was not investigated.

(Shagdar et al., 2017) carried out an analysis of

Cooperative Awareness Message (CAM) messages

for CACC using the IEEE 802.11p standard. They

investigated the potential of the CAM for platooning

and demonstrated that V2V communication could fa-

cilitate the formation of a stable platoon in highway

scenarios. They compared their analytical results with

those obtained from NS-3, but their focus was on the

impact of road density on communication, rather than

on the effects of noise.

In (Bae et al., 2020), the researchers evaluated

the performance of Dedicated Short-Range Commu-

nication (DSRC) based V2X technology on real test

tracks in multiple Line-of-sight (LoS) and NLoS sce-

narios. They assessed various key performance indi-

cators (KPIs), such as Packet Error Rate (PER), Re-

ceived Channel Power Indicator (RCPI), Packet Re-

ception Rate (PRR). The research proved that the

PRR is signiﬁcantly higher in LoS scenarios. Authors

also emphasize that in the future they would like to

evaluate the performance of the next generation short-

range communication protocol like 802.11bd which

will provide services for autonomous driving such as

sharing sensor information. To evaluate this large

messages will require new methods.

Knowle et al. (Knowles Flanagan et al., 2021) in-

vestigated V2V communication performance and its

impact on safety distance. They were able to demon-

strate the importance of a reliable network as a key to

reducing stopping distance and reaction time.

Fitah et al. (Fitah et al., 2018) evaluated the per-

formance of 802.11a (Wi-Fi) and 802.11p (DSRC)

communication protocols for Intelligent Transport

Systems. Their results showed that the DSRC proto-

col outperformed the Wi-Fi protocol in the scenarios

they performed. They used multiple simulation tools

like ns-3 for network and SUMO for trafﬁc simula-

tion, but for a safety critical application vehicle dy-

namics is essential which was not performed.

In (Rayamajhi et al., 2018), the authors conducted

three different experiments to investigate the perfor-

mance of DSRC in real-world and on CACC. They

considered thoughput, latency and characteristics of

the packet loss process for these experiments which

they validated through laboratory and experimental

tests. However, they are not focused on additional

interference effect on CACC.

In this book chapter (Fallah and Gani, 2018) the

authors evaluated the high ﬁdelity DSRC physical

layer modelling tools and the radio channel modelling

approaches, including the discussion about channel

propagation models (path loss, shadowing and fading

effects). However, the impact of vehicle dynamics on

wireless communication was not considered.

In (Mannoni et al., 2019), the group of researchers

compared the Cellular and DSRC technology and ex-

amined the network physical layer. They also applied

widely used indicators for the evaluation such as PER,

SNR and latency for both version of V2X. Also they

evaluated the vehicle density on the communications

and when its increasing, then the performance gap is

reduced between Cellular and DSRC.

Lian Cui and colleagues (Cui et al., 2018) evalu-

ated a CACC under cyber attack and proposed a simu-

lation platform to examine the effects of such attacks.

The research considered multiple parameters, includ-

ing vehicle dynamics, sensor errors, and communica-

tion latencies. The platform proposed in the paper is

capable of quantifying the crash severity. The conclu-

sion notes that cyber attacks do not always result in

Investigating the Safety Effects of Degraded Wireless Performance on Connected Longitudinal Driver Assistance Functions

279

crashes, and that GPS jamming is the most dangerous

cyber-attack for CACC-based platoons.

Most studies focus on latency and other parame-

ters that affect the network, and do not directly con-

sider the impact of noise levels, which are often the

cause of random communication failures. This study,

therefore, aims to investigate the impact of the SNR

on the physical transmission process (link-level per-

formance), with a speciﬁc focus on the packet deliv-

ery ratio (PDR).

The objective of this research is to examine the

various modulation effects, path loss, shadowing and

other noise factors that can impair wireless communi-

cation performance.

1.1 Main Contribution

The primary contribution of this work is the devel-

opment of a MATLAB Simulink-based DSRC simu-

lation platform that enables the evaluation of driver

assistance functionality under degraded wireless net-

work conditions in safety-critical scenarios. On the

other hand, a signiﬁcant contribution is the impact

analysis of the SNR on the number of packets deliv-

ered, providing insight into how different SNR levels

affect the reliability and efﬁciency of safety-critical

data dissemination. For CACC application, reliability

and latency are critical performance metrics. Based

on 5GAA technical report (5G Automotive Associa-

tion (5GAA), 2023), reliability is deﬁned as 99.9%,

ensuring that almost all control messages are success-

fully delivered to maintain seamless and synchronized

vehicle coordination. In addition, the latency require-

ment for acceleration and deceleration control is set

at 10 ms, emphasizing the need for ultra-low delay

to support real-time responsiveness and safety in dy-

namic trafﬁc scenarios. The technical report speciﬁes

C-V2X service level requirements, but is also appli-

cable to DSRC.

2 METHODOLOGY

In this research we used Matlab Simulink 2023b to

simulate a DSRC communication physical layer to

perform V2V communication. MATLAB Simulink is

well-suited for the analysis of the impact of degraded

V2V communication on driver assistance functions.

This is due to its ability to seamlessly integrate de-

tailed communication models with vehicle dynamics

and control systems in a single environment. This en-

ables precise simulation of time-sensitive interactions

between communication delays, packet loss, and driv-

ing behaviour. The following ﬁgure presents the four

main blocks of our model which performs the simula-

tion.

Figure 1: Simpliﬁed methodology diagram.

2.1 Vehicle Dynamics Model

The vehicle model used for this research is a six-

degree-of-freedom model, powered by a single elec-

tric motor on the rear axle. This model was selected

to enable the simulation of more complex vehicle dy-

namics in a variety of scenarios and for future re-

search. In addition to the dynamics, the vehicle con-

trol is also located here, where it performs the adap-

tive longitudinal control of the vehicle under test. In

this paper, we utilise a CACC system, which receives

inputs from a wireless network and subsequently con-

verts the acceleration data into torque, which is then

fed back into the vehicle dynamic model to calculate

the vehicle speed and position.

2.2 Scenario Management Module

Figure 2: Implemented scenarios.

This block is responsible for the initialization and ex-

ecution of all scenarios. It simulates the positions and

velocities of the different actors and cooperates with

the vehicle dynamic block. For the purposes of our

research, we conducted multiple scenarios to evaluate

the impact of varying SNR values on network perfor-

mance. Accordingly, testing under degraded commu-

nication conditions is important is that we need reli-

able information on how much we can rely on V2X

communication while the other environmental sen-

sors are partly or completely unavailable. In the sce-

narios presented in 2, only V2X wireless commu-

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280

nication was used, as the focus of this research was

to evaluate the effectiveness of wireless communica-

tion with added interference without the presence of

additional environmental sensors. In these scenarios,

we distinguished between two vehicles, which we de-

ﬁned as the ”host vehicle” (HV) and the ”remote ve-

hicle” (RV).

In order to simulate the effects of noise on the per-

formance of the network, we have considered a range

of different noise levels. This allows us to evaluate the

impact of noise on a connected driver assistance fea-

ture. The additional noise levels that we have chosen

are as follows: 0 dB, 10 dB, 20 dB, 25 dB.

2.3 DSRC Communication

This section serves as the central area of our re-

search, focusing on facilitating wireless communica-

tion among vehicles. For the simulation, we created

a basic frame which contains the necessary parame-

ters. These include the 3-dimensional positions, ve-

locities, angular velocities and the roll, pitch, yaw

indexes. Furthermore, it contains the actor IDs and

timestamps, with 3 additional slots for other param-

eters. These 18 parameters were then translated into

16-bit integers. In terms of bit size, this correlates

with the size of a CAM basic container ﬁeld, where

the vehicle parameters are stored (ETSI, 2014). Ini-

tially, we need to establish the methodology for com-

puting and adjusting the SNR value. Throughout our

study, the transmission power remained ﬁxed at 23

dBm, a default parameter for DSRC communication.

Since our main goal is to assess how SNR affects

packet delivery, it is crucial to determine the receiv-

ing power according to Eq. 1.

= P

− FSPL − X

(1)

FSPL = 10 · log



4 · π · d · f



(2)

The free space path loss (FSPL) (Ghasemi et al.,

2016) is a straightforward method for simulating the

message power attenuation between the transmitting

and receiving sides . In Eq. 2, we consider the dis-

tance between two antennas (d), the carrier frequency

( f

) and the speed of light (c). In the research we used

the single-slope model. Based on the measurements

of Kryszkiewicz et al (Kryszkiewicz et al., 2022) the

difference between the characteristics of a single- and

a double-slope model was insigniﬁcant. At distances

exceeding 200 meters, ground reﬂections begin to in-

duce modiﬁcations to the path loss model. This is

the reason why the focus of control is maintained be-

low this threshold, in order to ensure the safe opera-

tion of the CACC application. Shadowing (X

) uses a

log-normal random distribution with zero mean. For

our research we used 3.7 dB for standard deviation

(Kryszkiewicz et al., 2022). After we know the re-

ceiving Power we can ﬁnally calculate the SNR for

the communication (see in Eq. 3). This can be done

as follows:

SNR = P

− N

(3)

= N

thermal

+ N

other

(4)

The performance of background noise can be af-

fected by a number of factors, including ambient

noise and radio frequency interference. In the present

study, thermal noise was used as the main noise

source and its value was calculated as follows:

thermal

[dB] = 10 · log

(1000 · k · T · B) (5)

In order to conduct our study, we considered the

10 MHz bandwidth (B), which is dedicated for DSRC

communication, and an ideal 297 K temperature (T).

The last element of the equation is the Boltzmann

constant (k). This allows us to calculate the SNR

value by subtracting the noise from our receiving

power. In our study, we evaluated the impact of vary-

ing SNR values on network performance. To this end,

we considered additional interference (N

) values that

would degrade the network.

In the second stage of the process, the requisite

data must undergo an intricate series of transforma-

tions before reaching the ego vehicle. Our approach

involved following the IEEE 802.11p standard to es-

tablish the physical layer of DSRC communication in

Simulink, as demonstrated in Fig. 3.

Figure 3: Bit stream through physical layer.

Once the requisite data has been converted into

bits, forward error correction (FEC) is applied to

the bit stream, contingent on the chosen modulation

mode. In our paper we have selected 4 different mod-

ulations, as in Table 1, covering a signiﬁcant transmis-

sion distance in the operating range of DSRC commu-

nication.(Bazzi et al., 2017)

Table 1: Different modulations.

Mode Coding rate and modulation Receiver sensitivity [dB] LoS range [m]

1 1/2 QPSK -82 541

2 3/4 QPSK -80 439

3 2/3 64QAM -69 139

4 3/4 64QAM -68 125

Investigating the Safety Effects of Degraded Wireless Performance on Connected Longitudinal Driver Assistance Functions

281

Following the FEC coding, interleaving is em-

ployed to prevent the occurrence of long sequences

of adjacent noisy bits. The required modulations are

then applied to the bitstream before it is transmitted to

the channel. The chosen Orthogonal Frequency Divi-

sion Multiplexing (OFDM) modulation (see Table 2

is applied last, prior to transmission. In order to sim-

ulate the effects of environmental noise on the mes-

sage, we have incorporated additive white Gaussian

noise (AWGN) into the channel. The default parame-

ters for OFDM modulation were employed, in accor-

dance with the standard speciﬁed in 802.11p (IEEE,

2012).

Table 2: OFDM modulation parameters.

Data subcarriers 48

FFT length 64

Guard bands (left-right side) 6-5

DC null yes

Pilot subcarriers (positions) 4 (12, 26, 40, 54)

Cyclic preﬁx 16

OFDM symbol 6 (QPSK), 2 (64QAM)

At the receiving end, the process of decoding the

message is opposite to that of encoding. Initially,

the receiving device assesses the message reception

power and, if it is below the receiving sensitivity

threshold, our simulation assumes that the message

has not been received. Conversely, if the reception

power is above the sensitivity threshold, the device

attempts to decode the message. Decoding is a cru-

cial aspect of this layer, as it enables the recipient to

obtain the information they require from the sender.

2.4 Network Monitoring Module

The network monitoring module performs the calcu-

lation of network performance metrics like PDR or

PRR for the whole process. The PDR provides a last

second mean value, which is essentially a sliding win-

dow method-based aggregation. Without this module,

it would not be possible to evaluate the network per-

formance. In the future, this could be expanded to

include communication delays and other parameters,

which could be essential for testing V2X-based con-

trol solutions and support application testing and val-

idation.

3 RESULTS AND DISCUSSION

In our research, we conducted multiple scenarios to

assess the impact of varying SNR values on network

performance and to examine their effects on differ-

ent modulations. Each scenario involved a simulation

time of 50 seconds (CAM transmit frequency was 10

Hz, resulting in 500 packets transmitted) and com-

menced with the HV’s initial velocity set at 20 m/s.

Subsequently, in the CACC system, the HV acceler-

ated to a predeﬁned speed according to the scenario.

To ensure the smooth functioning of the CACC sys-

tem, the algorithm assumes that a vehicle is leading

at a given distance (in our case, 300 meters) if no

neighboring vehicles are actively transmitting mes-

sages. Once the ﬁrst packet is successfully received,

the perception module can then calculate the relative

distance between the RV and HV positions. To ad-

dress the fact that CACC alone is not designed to

bring the vehicle to a complete stop, we incorporated

a basic Autonomous Emergency Brake (AEB) into the

model. This functionality triggers when the time-to-

collision (TTC) between the vehicle and an obstacle

ahead is less than 1.8 seconds. Upon activation, the

AEB applies full brake force, halting the vehicle and

maintaining it in a stationary position until the end of

the simulation.

Orange highlighting in Table 3 and Table 4 shows

where even AEB could not stop in time.

3.1 Moving Target with Constant

Velocity

In our research, we aimed to provide a more realistic

use case scenario for CACC. The results of the net-

work analysis are presented in Table 3. In this sce-

nario, the velocity difference between the two vehi-

cles was 13.89 m/s. Initially, under stable network

conditions, all modulations yielded similar results,

and the CACC system effectively adjusted to the ve-

locity of the RV, as illustrated in Fig 4. Subsequently,

we intentionally introduced gradual degradation into

the network. At an additional 10 dB of noise, the

QPSK modulation remained unaffected. However,

with the 64QAM modulation, there was a noticeable

delay in receiving the ﬁrst packet. The system re-

ceived the ﬁrst correct message at a distance of about

100 meters. Despite this delay, the CACC system was

still able to respond adequately, thus averting a poten-

tial collision. As the noise level increased to 20 dB,

a number of signiﬁcant changes were observed. The

reception distance for QPSK modulation was found

to be halved, while there was also observable shifting

among different coding rates. With 64QAM modula-

tion, the reception distance dropped below 25 metres,

necessitating intervention from the AEB. A notable

difference was observed between the coding rates.

While a coding rate of 2/3 enabled the vehicle to stop

in time, a rate of 3/4 resulted in the vehicle being un-

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282

Figure 4: Host Vehicle Velocity and Relative Distance - Moving scenario.

able to stop even with AEB activation. Further de-

grading the network to an additional noise level of

around 25 dB, the AEB activates in all scenarios. This

occurs because the CACC system lacks the capability

Table 3: Results - Moving remote vehicle (HV velocity 25

m/s, RV velocity 11.11 m/s. Sent packets 501, Simulation

time 50 seconds).

Received

packets

PRR

[%]

First

reception

distance

[m]

min

TTC

[s]

Coding rate

and

modulation

Inter-

fer-

ence

level

501 100.0 200.5 3.77 1/2 QPSK

0 dB

501 100.0 200.5 3.77 3/4 QPSK

466 93.0 198.6 3.85 2/3 64QAM

458 91.4 198.6 3.86 3/4 64QAM

492 98.2 198.6 3.85 1/2 QPSK

10 dB

479 95.6 198.6 3.85 3/4 QPSK

396 79.0 87.1 3.62 2/3 64QAM

385 76.8 87.1 3.66 3/4 64QAM

412 82.2 101.7 5.18 1/2 QPSK

20 dB

398 79.4 89.1 3.97 3/4 QPSK

43 8.6 22.7 0.96 2/3 64QAM

57 11.4 13.6 0.22 3/4 64QAM

88 17.6 57.4 1.76 1/2 QPSK

25 dB

66 13.2 43.3 1.64 3/4 QPSK

47 9.4 13.1 0.22 2/3 64QAM

46 9.2 8.6 0.15 3/4 64QAM

to adapt to the velocity of the leading vehicle in time.

While accidents are avoided during QPSK modula-

tion, only the impact velocity can be mitigated with

QAM modulation.

3.2 Stationary Target

In this test, our aim was to simulate a worst-case sce-

nario where the RV is stationary, allowing us to as-

sess the maximum reception distance of various cod-

ing schemes. Similarly with the previous scenario, the

results are presented by Table 4. When the SNR re-

mained unaffected by additional noise, clear distinc-

tions between different modulations emerged. QPSK

modulation successfully received the ﬁrst message

at approximately 500 meters, which represents the

maximum distance between the two vehicles. How-

ever, 64QAM modulation only managed to receive

the message at around 200 meters. The difference be-

tween coding rates was minimal, with only a slight

variation observed in PRR, amounting to just a few

percentage points. As the network became noisier,

reception deteriorated signiﬁcantly. With a 10 dB in-

crease in noise, the reception distance halved, leading

to a noticeable decline in PRR. Further degradation

of the network resulted in delayed activation of safety

features. In the case of the more complex 64QAM

modulation, from a 20 dB increase in noise, the sys-

tem failed to stop the vehicle in time, even with AEB

intervention. The high PRR values observed in these

scenarios can be explained by the fact that the AEB

stopped the vehicle in the reception zone (regardless

of whether a collision occurred), therefore the HV

Investigating the Safety Effects of Degraded Wireless Performance on Connected Longitudinal Driver Assistance Functions

283

Figure 5: Host Vehicle Velocity and Relative Distance - Stationary scenario.

continued to receive messages after reaching the sta-

tionary position. In case of highest additional noise

the HV stopped outside of reception zone resulting in

low PRR.

Table 4: Results - Stationary remote vehicle (HV velocity

20 m/s, Sent packets 501, Simulation time 50 seconds).

Received

packets

PRR

[%]

First

reception

distance

[m]

min

TTC

[s]

Coding rate

and

modulation

Inter-

fer-

ence

level

498 99.4 497.6 1.53 1/2 QPSK

0 dB

493 98.4 495.6 1.52 3/4 QPSK

333 66.5 206.0 5.60 2/3 64QAM

326 65.1 206.0 5.60 3/4 64QAM

383 76.4 340.2 1.71 1/2 QPSK

10 dB

365 72.9 272.1 1.76 3/4 QPSK

281 56.1 91.7 1.48 2/3 64QAM

288 57.5 91.7 1.48 3/4 64QAM

293 58.5 103.7 1.59 1/2 QPSK

20 dB

282 56.3 75.6 1.25 3/4 QPSK

261 52.1 25.5 0.15 2/3 64QAM

253 50.5 25.5 0.15 3/4 64QAM

275 54.9 51.6 0.47 1/2 QPSK

25 dB

272 54.3 47.6 0.56 3/4 QPSK

19 3.8 11.5 0.12 2/3 64QAM

12 2.4 11.5 0.12 3/4 64QAM

4 CONCLUSION

In our study, we investigated the impact of variations

in SNR on transmitted packets and examined the ef-

fect of different noisy network conditions on a safety-

focused driver assistance feature (CACC). Our ﬁnd-

ings align with previous research, indicating that as

SNR decreases, the operational range of more com-

plex modulations also decreases. Furthermore, we de-

veloped a simulation environment suitable for future

V2X-based developments. In the future, our objective

is to further improve the available control concepts

utilising network performance metrics to mitigate the

severity of various scenarios. Furthermore, we in-

tend to expand our simulation platform to include la-

tency assessment throughout the packet sending pro-

cess. This extension will provide a more comprehen-

sive understanding of the system’s performance and

aid in developing future V2X systems. The forthcom-

ing iteration of the proposed system may be utilised

in scenarios of greater complexity. This would re-

quire the simulation of transmission in a more dense

environment, thereby enabling an investigation into

the manner in which the diverse wireless communic

ation-based driver assistance systems respond to al-

terations in their surrounding environment.

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284

ACKNOWLEDGEMENTS

This work was supported in part by the Ministry

of Innovation and Technology National Research,

Development and Innovation Ofﬁce (NRDI) within

the framework of the Autonomous Systems National

Laboratory Program RRF-2.3.1-21-2022-00002.

Supported by the

UNKP-23-4-I-BME-158 New

National Excellence Program of the Ministry for Cul-

ture and Innovation from the source of the National

Research, Development and Innovation Fund.

The study was prepared with the professional sup-

port of the Doctoral Student Scholarship Program of

the Co-operative Doctoral Program of the Ministry

for Innovation and Technology from the source of

the National Research, Development and Innovation

Fund.

This work was supported by Jaguar Land Rover

Hungary Ltd organization.

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