Concept of Smart Infrastructure for Connected Vehicle Assist and Trafﬁc

Flow Optimization

Shiva Agrawal

1,∗

, Rui Song

2,∗

, Akhil Kohli

, Andreas Korb

, Maximilian Andre

, Erik Holzinger

and Gordon Elger

1, 2

Institute for Innovative Mobility (IIMo), Technische Hochschule Ingolstadt, Germany

Fraunhofer IVI, Applied Center Connected Mobility and Infrastructure, Ingolstadt, Germany

EFS - Elektronische Fahrwerksysteme GmbH, Ingolstadt, Germany

eMundo GmbH Ingolstadt, Ingolstadt, Germany

{andreas.korb, maximilian.andre, erik.holzinger}@e-mundo.de

Keywords:

Road Side Unit (RSU), Smart Infrastructure, Radar, LiDAR, Camera, Perception, Sensor Data Fusion,

Vehicle-to-everything (V2X), Communication, Simulation, ITS.

Abstract:

The smart infrastructure units can play a vital role to develop smart cities of the future and in assisting auto-

mated vehicles on the road by providing extended perception and timely warnings to avoid accidents. This

paper focuses on the development of such an infrastructure unit, that is speciﬁcally designed for a pedestrian

crossing junction. It can control trafﬁc lights at the junction by real-time environment perception through its

sensors and can optimize the ﬂow of vehicles and passing vulnerable road users (VRUs). Moreover, it can

assist on-road vehicles by providing real-time information and critical warnings via a v2x module. This paper

further describes different use-cases of the work, all major hardware components involved in the development

of smart infrastructure unit, referred to as an edge, different sensor fusion approaches using the camera, radar,

and lidar mounted on the edge for environment perception, various modes of communication including v2x,

system design for backend and requirement for safety and security.

1 INTRODUCTION

The development of autonomous vehicles is currently

one of the biggest trends and challenges in the auto-

motive industry. In order to achieve the mission of

zero road accidents and to ease the journey of people,

a lot of companies around the world are investing both

money and time to develop advanced technology. One

of the important parts of autonomous vehicles is their

capability to perceive and predict other road users’

behavior and motion. This helps to predict the next

movement of the vehicle itself. For this purpose, such

vehicles are equipped with multiple sensors like cam-

eras, lidar, radar at different positions. The data from

each sensor is ﬁrst individually processed and later

fused together to get an accurate environment view of

both static and dynamic surrounding objects.

Due to the complex structure of cities and the in-

crease of road users including vehicles and humans,

perception from the vehicle itself is not sufﬁcient in

*The First Two Authors Contributed Equally.

many situations. In addition, it is also difﬁcult for

the vehicle to get information about next danger situ-

ations, blocked or damaged roads, states of the trafﬁc

light in next signals, passing of emergency vehicles,

etc. well ahead of the time without support from ex-

ternal sources.

One of the solutions to these issues can be the

development and deployment of smart infrastructure

units alongside the roads. Such units consist of multi-

ple sensors to perceive the environment and commu-

nication modules to provide helpful information and

timely warnings to vehicles on the road.

In recent years, project work to developed smart

infrastructure and connected mobility has started in

different regions as described in (RWTH Aachen

University - ika, ), (BW-test ﬁeld, ), (providentia++, )

and (dai-labor TU Berlin, ). One of the projects to set

up a test ﬁeld for smart infrastructure and connected

mobility is in development in Ingolstadt (Agrawal

and Elger, 2021). The work described in this paper

is well connected with this research project but with a

360

Agrawal, S., Song, R., Kohli, A., Korb, A., Andre, M., Holzinger, E. and Elger, G.

Concept of Smart Infrastructure for Connected Vehicle Assist and Trafﬁc Flow Optimization.

DOI: 10.5220/0011068800003191

In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2022), pages 360-367

ISBN: 978-989-758-573-9; ISSN: 2184-495X

special focus to control the trafﬁc lights on the pedes-

trian crossing junction to optimize the trafﬁc ﬂow in-

cluding vehicles and venerable road users (VRUs) as

highlighted in ﬁgure 1. Other use-cases of this work

are stated in a later section.

As shown in ﬁgure 1, the yellow lane is the vehicle

road (two lanes) and the narrow pink lane is the pedes-

trian and bicycle track. The trafﬁc ﬂow at the cross-

ing is managed by trafﬁc lights as highlighted by the

red box. One smart infrastructure unit referred to as

an edge is developed to perceive the pedestrian cross-

ing junction and nearby lanes. This edge is equipped

with a high-resolution Radar, high-resolution LiDAR,

multiple RGB cameras, and one v2x communication

module. The edge is connected with a central back-

end system which acts as a ﬁnal decision-maker. In-

formation about road users is sent by the edge to the

backend using a dedicated communication protocol

and then the backend controls the trafﬁc lights ac-

cordingly. Further backend generates critical warn-

ings and other information signals which are com-

municated to vehicles through the v2x module of the

edge.

Figure 1: Smart infrastructure location.

This paper is structured as follows: Section II

highlights the main use-cases of the work, section

III introduces the architecture of the complete system

and further describes the details of each component

and then Section IV provides the conclusion and fu-

ture work.

2 USE CASES

The work focuses on two main use-cases

1. Trafﬁc ﬂow optimization – It means that depend-

ing on the real-time trafﬁc on the vehicle lane

and on the pedestrian lane, the trafﬁc lights are

switched on/off at the crossing junction by the

backend. The real-time trafﬁc information is per-

ceived, processed, and send by the edge to the

backend to make decisions. This can further also

include prioritization of the emergency vehicles.

2. Assisting vehicles on the road – It means prov-

ing real time information and/or critical warn-

ings/alerts to the passing by vehicles via V2X

communication. This includes to

• Send timestamp information about the current

and next state of the trafﬁc light to allow vehi-

cles to pass efﬁciently with less braking.

• Send warning to vehicles if some non-VRU like

an animal, football, or other object detected in

the vehicle lane around the crossing junction

• Send the maximum speed limit info of the area

to passing vehicles and also to send a warning

if the speed limit is violated.

• Send a signal to an emergency vehicle in case

of an accident in the monitoring area

• Send warning to other vehicles in case some

emergency vehicle is passing from the monitor-

ing area.

3 ARCHITECTURE AND THE

MAIN COMPONENTS

The Development of smart infrastructure involves the

active and ﬂawless interaction of multiple compo-

nents. These components are both software as well as

hardware. To introduce all these major components,

the high-level architecture of the complete system is

shown in ﬁgure 2. These include

Figure 2: High-level architecture.

1. Edge – pole-like structure unit as shown in Fig-

ure 3, where multiple sensors and V2X module

are mounted and calibrated. This is used for envi-

ronment perception through sensor data fusion in

real-time at the crossing junction.

Concept of Smart Infrastructure for Connected Vehicle Assist and Trafﬁc Flow Optimization

361

2. Backend – the central system which receives the

data from an edge (could also be multiple edges

in the future) and also from on-ﬁeld vehicles via

edge. It decides when to change the states of traf-

ﬁc lights and also when and which information or

warning to send to passing by vehicles.

3. Trafﬁc light controller and trafﬁc lights – this can

be considered as the ﬁnal actuating component

which receives the control signal from the back-

end and changes the states of one or more traf-

ﬁc lights accordingly, to optimize the trafﬁc ﬂow

at the crossing junction. In case, communication

with the backend fails, then the trafﬁc controller

runs the trafﬁc lights in default time-based mode.

4. Communication – even though this component

is not so effectively visible in ﬁgure 2, it is the

backbone of the complete system. This module

includes two types of communication - between

the edge and the backend via SENSORIS and

between vehicles and the edge through the V2X

module (CPM, CAM, and DENM protocols).

5. V2X enabled vehicles – test vehicles that are an

active part of the system to test all the use-cases

which include assisting through infrastructure.

Details of each component – the edge, the back-

end, trafﬁc light controller, communication, safety,

and security are described further in this section. V2X

enabled vehicles are currently considered outside the

scope of this paper as they are third-party vehicles and

do not involve active development in the scope of the

work.

3.1 Edge

The smart infrastructure unit that comprises multiple

sensors and a v2x module is known as the edge. For

the current work, as stated before one edge is devel-

oped for research and testing purposes. As shown in

ﬁgure 3, the edge has three main components 1. Mast

– pole like mechanical structure 2. Multiple sensors

and v2x module mounted on top of the mast 3. Con-

trol cabinet – contains all the other required comput-

ing, control, and power supply components (located

on the ground).

Environment perception using edge means detec-

tion, classiﬁcation, and tracking of the vehicles and

VRUs. There are three main broad categories of ob-

jects around crossing junction which has to be accu-

rately perceived. One is vehicles which include cars,

trucks, buses, vans, emergency vehicles, etc. which

travel through the vehicle lane across the junction.

The second category is VRUs which include walk-

ing person (adult or child), jogger, bicyclist, e-scooter

traveler, person walking with child carriage, group

of people (two or more), etc. which travels through

pedestrian lane across the junction. The third cate-

gory is non-VRUs – includes unknown large objects,

animals, etc. whose path is unknown and random.

Figure 3: Edge.

As per the geography of the selected pedestrian

crossing junction, it is required to detect and classify

VRUs up to 50 m from the junction on either side and

to detect and classify vehicles up to 100 m from the

junction on either side. Further detection and classiﬁ-

cation of non-VRUs are required when they are very

close to the junction (around 20 m on either side) and

probably can obstruct the trafﬁc ﬂow.

As the task of infrastructure-based perception

shares the common goal with most of the autonomous

vehicle’s perception pipeline, it is wise to use the sen-

sors developed for the autonomous vehicles. Keeping

this in mind, the most widely used and matured tech-

nology of sensors - LiDAR, Radar, and RGB cameras

are selected.

The selection of a speciﬁc model and the man-

ufacturer for the individual sensor is carried out by

ﬁrst doing an extensive search to select best-ﬁt sen-

sors as per price and availability. Later, a decision

matrix based on all the requirements for each selected

model of sensor type is developed. As per the out-

come of the decision matrix, the best of two or three

sensors are selected. At last, selected sensors were

mounted on the mast of the lab setup and tested for

ﬁnal analysis. With the comparative analysis for each

model, sensors of each type are ﬁnalized for the re-

search work.

The hardware architecture of the Edge is shown in

ﬁgure 4.

Each sensor perceives the environment within its

VEHITS 2022 - 8th International Conference on Vehicle Technology and Intelligent Transport Systems

362

Figure 4: Edge hardware architecture.

Field of View (FoV). As shown in ﬁgure 4, LiDAR,

Radar, and camera units are connected to the switch

using Ethernet 1G connection. Further, the switch is

connected to the central application computer which

does all the software development of the edge. All

the sensors are powered using a 12 or 24 V DC power

supply. The switch is powered using a 48V DC power

supply. The application unit is a high computing

desktop computer that consists of multi-core CPUs

and dedicated GPU to develop and deploy AI algo-

rithms for sensor fusion.

The goal of this research in the direction of en-

vironment perception is to detect, classify and track

vehicles and VRUs in different weather and light con-

ditions. As each sensor has its pros and cons, to com-

plement them, sensor fusion is developed in the ap-

plication unit. Further, this work speciﬁcally aims to

develop and implement AI-based raw level sensor fu-

sion algorithms to fuse the raw data from two or more

sensors for ﬁnding optimum solutions and parame-

ters for different light and weather situations across

the junction. The raw level sensor fusion is selected

over object-level fusion to explore the beneﬁts of us-

ing complete data available from sensors.

As described in ﬁgure 5, raw data of LiDAR, i.e.

point cloud, raw data from cameras, i.e., RGB images,

and raw data from Radar, i.e., radar detection points

are acquired from sensors and sent to the application

unit at a pre-deﬁned data rate. At present, this data

rate is deﬁned as 10 Hz for each sensor. For the de-

velopment of a software framework, Robot Operating

System (ROS) is used.

To apply AI-based algorithms, labeled data is re-

quired. For this purpose, the sensors are mounted

on the lab mast using customized 3D mountings, and

then they are calibrated with each other and also with

edge to get the data in edge coordinate frame. Further,

all the sensors are synchronized together using com-

mon time reference before collecting the data in the

application unit. To label the sensor data, methods

involving both manual labeling and semi-automatic

labeling are used. Further speciﬁc scenarios with

pedestrians and vehicles equipped with GPS and IMU

systems are also designed to collect ground truth data.

In order to use the raw data directly as fusion, two

approaches are ﬁnalized after doing a literature survey

(Chadwick et al., 2019) (Chang et al., 2020). In the

ﬁrst approach, the radar point cloud is transformed

into an RGB plane, and values of radar, i.e. spatial

information (X, Y, Z) and measurement information,

i.e. range, doppler velocity, and RCS are encoded to

RGB values. Similarly, the lidar dense point cloud is

transformed and encoded into a separate RGB plane.

These results in 3 independent RGB planes, each from

radar, lidar, and camera for the same instance. These

are then at ﬁrst fed into a few separate CNN layers

to extract high-level features, then added together and

further passed through more CNN layers to ﬁnally get

the object position and class information.

In the second approach of the raw sensor fusion,

the radar and Lidar 3d point cloud data is encoded

into a separate 3D voxel grid. Then the 3d voxel grid

input is fed to 3D CNN layers to extract upper layer

features separately for radar and Lidar. Camera RGB

images are fed into 2D CNN layers. After extrac-

tion of high-level features, an intermediate later is de-

signed to transform the features in a common plane,

then added together and further trained using more

layers to ﬁnally extract object position and class.

The sensor fusion algorithm provides position,

speed, and class of objects which are further tracked

using ﬁlters. The ﬁnal track objects’ information is

sent to the backend. As per the perception informa-

tion processed and sent by edge, the backend takes

appropriate decisions to optimize the trafﬁc ﬂow and

also to assist passing vehicles by providing informa-

tion and/or warnings in real-time.

Figure 5: Edge software architecture.

3.2 Communication

An appropriate communication design enables the

promising information exchange among all intelligent

components in the entire system, which guarantees

the performance of the intelligent infrastructure-based

trafﬁc services and applications. In this paper, the

challenges of efﬁcient data sharing in communication

networks is speciﬁcally tackled, i.e., 1) highly hetero-

geneous networks for dissemination of various mes-

sages using V2V, V2C, V2I, etc. 2) variety of QoS

requirements in miscellaneous trafﬁc services.

Concept of Smart Infrastructure for Connected Vehicle Assist and Trafﬁc Flow Optimization

363

Table 1: Summary of QoS requirement for our use cases (UC) with respect to the speciﬁcations in 3GPP and 5GCAR.

Criteria UC 1: Connected Vehicle Assist UC 2: Trafﬁc Flow Optimization

Latency 3–100 ms second level

Reliability 99.999% 90%

Throughput 1 Gbps 25 Mbps

Message type CPM & CAM & SENSORIS DENM & SENSORIS

Communication range 40-70 m few kms

V2X communication type C2V & I2V & I2C V2C & I2C

Speed of UEs 0-70 km/h (urban) 0-70 km/h (urban)

Figure 6: Proposed protocol stack in communication solu-

tion.

3.2.1 Interoperable Communication Framework

ERTICO SENSORIS (SENSORIS Innovation Plat-

form, ) und ETSI C-ITS V2X messages (ETSI TS

103 301 V1.3.1, 2020) are two main communication

frameworks in Cooperative Intelligent Transport Sys-

tem (C-ITS), which are open, standardized, and com-

monly accepted (Song and Festag, 2021). Speciﬁ-

cally, ERTICO SENSORIS provides a sensor interface

between vehicles and the cloud via cellular communi-

cation. The detected objects in each vehicle equipped

with SENSORIS software can be uploaded in form of

the messages encoded using Google Protobuf. ETSI

C-ITS V2X messages is composed of a set of protocols

in the facilities layer of V2X protocol stack (Festag,

2015). By broadcasting various messages, e.g. Col-

lective Awareness Message (CAM), Cooperative Per-

ception Message (CPM), Decentralized Environmen-

tal Notiﬁcation Message (DENM), the C-ITSs can

share the sensor information in an ad-hoc network

over ITS-G5 or C-V2X in 5.9 GHz frequency band.

To exploit the information in the road trafﬁc and

interoperate the systems in hybrid networks together

with infrastructure and vehicle, we propose the pro-

tocol stack with both communication frameworks for

deployment, which is shown in Fig. 6. The main aim

to include both types of communication is to consider

possible interface compatibility for future expansion

of the project. Further, this will also help to gain suf-

ﬁcient experience and development of required soft-

ware stack.

In addition as shown in Fig. 6 on the left side, the

system is designed for the V2X protocol stacks. C-

V2X with PC5 interface resides on the access layer.

GeoNetworking (GN) distributes the packets in the

geographical ﬁeld and the basic transport protocol

(BTP) enables the multiplexing and demultiplexing of

messages on site of C-ITS. In the facilities layer, the

CAM, CPM, and DENM with corresponding ETSI

Service are employed for sensor data sharing. On

the right-hand side, LTE with uplink and downlink

(UL&DL) is set as the physical interface for commu-

nication to the backend. As the ﬁrst cloud deploy-

ment, the TCP/IP-based Google SubPub is integrated

for SENSORIS message dissemination. Both ETSI

and SENSORIS services are deﬁned as interfaces be-

tween application and facilities layers. Consequently,

the backend applications can take the actions depend-

ing on the information from the hybrid networks.

3.2.2 Individual QoS Design

The intelligent infrastructure system is aimed at pro-

viding vehicles with miscellaneous trafﬁc services

and applications to accomplish a safer and more ef-

ﬁcient road environment. Each trafﬁc service or

application addressing associated use cases requires

individual communication quality (Kanavos et al.,

2021)(Abdel Hakeem et al., 2020). Tab. 1 shows

the summary of Quality of Service (QoS) requirement

for our use cases (UC 1: Connected Vehicle Assist

and UC 2: Trafﬁc Flow Optimization) with respect

to the speciﬁcations in key international organizations

3GPP and 5GCAR (Condoluci et al., 2019).

Speciﬁcally in UC 1, the infrastructure can warn

the trafﬁc participants, if potential risks on the road

are detected by the edge system. According to the

speciﬁc warning functions, the service will require

a corresponding latency range from 3 to 100 ms

in communications. Very high reliability and large

throughput can ensure the warning signals are gener-

ated correctly and received by other C-ITS in time.

CPM, CAM, and SENSORIS messages are employed

to carry the sensor information in the heterogeneous

networks. While the efﬁciency of trafﬁc is opti-

VEHITS 2022 - 8th International Conference on Vehicle Technology and Intelligent Transport Systems

364

mized by controlling the trafﬁc signals at the pedes-

trian crossing in UC 2, where the latency should be

on the second level and high reliability, as well as

25 Mbps throughput are sufﬁcient for the related ser-

vices. DENM and SENSORIS carry the sensor infor-

mation and trafﬁc events, such as trafﬁc jams, and is

used by trafﬁc signal controller to take the appropriate

actions, and hence improve the trafﬁc efﬁciency.

3.3 Backend

The backend module of the architecture is the part

where the intelligent decisions for the trafﬁc lights are

made. It receives all road users detected by the Edge

and sends the recommended state of the trafﬁc lights

based on them. Figure 7 depicts the inner architecture

of the Backend.

Google Cloud Platform (Pub/Sub)

Inbound

Adapter

State Machine

World

State

Service

Outbound

Adapter

Road User Cache

Decision Algorithm

Figure 7: Simpliﬁed inner architecture of the backend.

The message with the information about the cur-

rent road users is received by the inbound adapter. Af-

ter it is deserialized and validated, it is forwarded to

the World State Service. In this service, the road user

state will be stored in the Road User Cache. The cur-

rent trafﬁc situation is derived from the Road User

Cache and subsequently, send to the Decision Algo-

rithm, which returns the recommended trafﬁc light

event. This event is transferred to the State Machine.

When the state has changed, the State Machine passes

the new state to the Outbound Adapter. Here the

new state is processed and published to the Google

Pub/Sub system.

The Road User Cache represents the overall sit-

uation at the junction. Each contained road user is

identiﬁed by a speciﬁc ID. Additionally, it speciﬁes

the following data - position, speed, conﬁdence of ex-

istence, type, and conﬁdence of type.

The Decision Algorithm takes the current junction

state and these road users as input and generates a

junction event as output. Currently, two different ap-

proaches for the calculation are being evaluated.

Algorithm I: Self-developed Algorithm. It consid-

ers many situations and creates the result determin-

istically. This includes situations where pedestrians

only, cars only, or both are waiting or emergency traf-

ﬁc such as ambulances are present.

Pedestrians

can cross

Vehicles can cross

Yes

SRU present?

Yes

VRU present?

Yes

NVRU present?

Yes

VRU waited

enough?

SRU := Special Road User (Emergency vehicle)

VRU := Vulnerable Road User (Pedestrian, Cyclist)

NVRU := Non Vulnerable Road User (Car)

Figure 8: Simpliﬁed static algorithm with some details

omitted for brevity.

Algorithm II: Deep Learning. Furthermore, the sec-

ond algorithm is a deep neural network. Its training

happens through reinforcement learning, i.e. no train-

ing data is required. The network can be trained to

lower the waiting time or – for a more environmen-

tally friendly approach – the CO

emission of a road

user at the junction. The beneﬁts of such a Deep Re-

inforcement Learning approach could be:

• The neural network learns over time and can make

decisions based on historic data.

• It can also adapt to new circumstances without the

need to rewrite the algorithm.

• This autonomous dynamic learning process can

optimize the algorithm even more than a static

hard-coded decision procedure.

This approach bears some challenges mainly concern-

ing the validation of the different results of the net-

work. There must be mechanisms to outvote the neu-

ral network, e.g., to ensure that there is not a starva-

tion problem for one of the sides.

Concept of Smart Infrastructure for Connected Vehicle Assist and Trafﬁc Flow Optimization

365

Both strategies (Algorithm I & II) have to be

tested extensively.

3.4 Trafﬁc Light Control

The trafﬁc light controller module is the ﬁnal compo-

nent to optimize the trafﬁc ﬂow at the crossing junc-

tion. For the two main lanes - the vehicle lane and

the pedestrian lane, two different trafﬁc lights are se-

lected. These are used for the ﬁnal demonstration of

the complete system. The trafﬁc light module with

two lights – green and red is used for the pedestrian

lane and the trafﬁc light module with three lights –

green, amber, and red is used for the vehicle lane.

The main components of the trafﬁc light controller

module are DC power supply unit, DC Splitter, 8 in-

put DC relay board, raspberry pi as the control unit,

and two trafﬁc lights modules.

Figure 9: Trafﬁc light controller block diagram.

The block diagram shown in ﬁgure 9 highlights

the ﬂow of signals for trafﬁc light control. The back-

end which resides in the cloud connects itself via the

internet to the controller. The python API running in

the controller reads the decision from the backend and

accordingly sends the signal to switch ON or OFF one

or more trafﬁc lights via serial communication to the

relay circuit. At last relay circuit activates or deacti-

vates the respective trafﬁc light.

3.5 Safety and Security

Networking and digitization are unthinkable with-

out information security. V2X communication, au-

tonomous driving, and digitization are just a few ex-

amples of future topics that are being incorporated

into the development of a new generation of vehicles

and infrastructure. The increasing networking of ve-

hicles and intelligent infrastructure not only increases

the complexity, but also the vulnerability of such sys-

tems to cyber-attacks.

The major subsystems of this project, i.e., the edge

and the backend, and the communication interfaces

between them are at a risk from a safety and security

perspective.

A system is at risk with one of the impact cate-

gories - operational readiness/capability, safety, pri-

vacy, and ﬁnancial impact.

In addition to safety, data from the sensors, such as

cameras at the trafﬁc lights, which are pre-processed

in the edge or during further processing in the back-

end, must be protected from unauthorized access.

To comply with safety and security requirements,

primarily the international standards, ISO/SAE 21434

(road vehicles cyber-security engineering) (ISO/SAE

21434:2021, ), ISO 26262 (road vehicles func-

tional safety (ISO 26262-1:2011, ) and ISO 27000

series (information technology security techniques)

(ISO/IEC 27000:2018, ) are considered. Using these

standards, the two major safety goals for this work

are derived. These safety goals states that smart in-

frastructure system should not send a false warning to

the vehicles and the trafﬁc lights at junctions should

be green in conﬂicting directions at the same time.

4 CONCLUSIONS

This paper addresses the challenges in the intelligent

transport system and focuses on infrastructural so-

lutions. Based on a thorough analysis of two use

cases - Connected Vehicle Assist and Trafﬁc Flow Op-

timization, the concept of an intelligent infrastruc-

ture system is proposed, which enables trafﬁc data

collection through perception and V2X communica-

tion. Through data fusion at the roadside edge com-

puters, trafﬁc safety and efﬁciency can be improved

by cloud-based backend via trafﬁc light control and

sending V2X messages to connected vehicles. The

safety and security of the entire system have been an-

alyzed, which ensures the success of future deploy-

ment and testing on public roads.

ACKNOWLEDGEMENTS

This work is supported by the Bavarian Ministry of

Economic Affairs, Regional Development and Energy

(StMWi) in the project “INFRA – Intelligent Infras-

tructure”. We would like to thank Mr. Sebastian

Mauthofer for feedback on safety and security regard-

ing the design of infrastructure systems.

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