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Faculty of Computer Science and Networking Department of Computer Science

Implementation and Performance Evaluation of An

Emergency Vehicle Warning System

By Ismael Abdulai

THESIS

Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science and Networking offered jointly by the University of Pisa and Sant’Anna School of Advanced Studies, 2020

Supervisor

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ABSTRACT

Advancements in vehicular technologies have come a long way into trying to ensure complete safety to humans and properties alike. Automotive transport systems consequently raise questions of safety with regards to vehicle occupants as well as road users alike. It is in answering these questions that bring to life the various forms of advanced technology we have today. Though not at the apex of the technological journey, this thesis creates an emergency vehicle

warning system in which drivers on a network are informed of an

oncoming emergency system through the Vehicle-to-Vehicle (V2V) as well as other roads users through the Vehicle-to-Everything (V2X) application system. This is under the area of intelligent transport system (ITS) where communication between devices is enabled on our roads thus ensuring all vehicles within a certain mile radius is aware of any emergency vehicles coming their way [1].

The awareness of this system cannot be reliant on the individual sensors of these automated vehicles but would have to make a connected communication with other vehicles for cooperated messaging. As a result of such communications, messages will have to be reliable and received within the shortest possible time with little delay. 5G which is the latest mobile technology ensures improved performance with a low latency and increased reliability and higher throughput under higher connectivity density. This project utilizes 5G technology for messaging [2].

In this thesis, I explain the underlining technology as its used as well as its performance with regards to communication latency and reliability and how they combine to give road users a better experience whilst saving time, life and property to enable the growth of the automotive industry.

Keywords: Automotive transport system, Vehicle-to-Vehicle(V2V), Vehicle-to-Everything(V2X), Intelligent Transport System (ITS), 5G technology.

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ACKNOWLEDGEMENTS

I would like to express gratitude to my thesis supervisor Professor Luca Valcarenghi

for his insightful scholarly input and feedbacks, patience, guidance and motivation in developing this thesis. Further, I would like to thank my family for providing me with endless support and encouragement throughout my years in school and through the process of researching in writing this thesis. This would not have been possible without them.

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Table of Contents

1. Introduction ... 6 2. 5G Communication ... 8 3. Categorization of V2X Applications ... 9 3.1 Safety ... 10 3.2 Convenience ... 11

3.3 Advanced Driving Assistance ... 11

3.4 Vulnerable Road User ... 12

4. Motivation ... 13

5. Limitation and Possible Drawbacks ... 14

6. Related Work and Publications... 18

7. Problem Description and Objectives ... 24

8. Road Safety and Emergency Services ... 26

9. Mobile Edge Computing in V2X Communications ... 30

10. Use Cases on Capabilities of Edge Computing ... 33

11. Technical Use Cases for V2X ... 36

11.1 Cooperative Maneuver... 37

11.2 Cooperative Perception... 38

11.3 Cooperative Safety ... 38

11.4 Autonomous Navigation... 40

11.5 Remote Driving ... 41

12. Technical Requirements and KPIs for V2X... 42

13. Implementation ... 49

13.1 CAM ... 50

13.2 CAM Messaging in Reference to ITS Architecture... 52

13.3 Algorithm of CAM Messages ... 54

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15. DENM ... 60

15.1 DENM Message Structure... 62

15.2 DENM Message Algorithm... 63

16. Result ... 65 17. Conclusion... 67 Reference ... 68

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1. INTRODUCTION

Automated vehicles are fast becoming the norm of an ever-evolving society that is increasing its use of data day by day in such vast proportions. The connection of vehicles to Internet of things (IoT) is increasing and opening a variety of avenues for the automotive industry. There is a transformation on how we interact with our vehicles as a pool of services becomes readily available to us. The world of technology and the automotive industry is merging and creating the ability for large amount of data to be shared over networks. This result is the potential of a wide range of advanced technological applications in our cars as well as on our roads. This leads to the phenomenon of Intelligent Transport Systems which encapsulates the V2X application for vehicular communication [3].

V2X, which stands for 'vehicle to everything', is the general term for vehicle communication system, where information from vehicles and other sources are shared or transmitted via high-bandwidth, low-latency, high-reliability links, paving the way to a variety of possible technological outcomes not excluding autonomous driving [4].

There is a significant increase in traffic which is causing huge problems for road users especially in urban areas. This raises the number of road fatalities which is a problem that is being tackled all over the world. The automotive industry has tried to reduce the amount of carnage on our roads by implementing some measures in vehicles that could reduce fatalities. Also, some road traffic management systems have been tried to curb this menace. But with the advancement of technology, there is a need to tackle this issue on a different level and one of such is the communication between vehicles and road users on our roads. The development of Vehicle-to-Everything Communication (V2X) is one of the major developments that has the capabilities to reduce congestions as well as accidents. Within the V2X communications is imbibed different forms of communications between vehicles and other things. There are several components of V2X, among these include vehicle (V2V), infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N) communications. Vehicle-to-Vehicle

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communication (V2V) implies the direct communication between vehicles. Information is sent from one vehicle to another directly or through an intermediate infrastructure. Vehicle-to-Infrastructure communication (V2I) refers to shared information between vehicles and so-called roadside units (RSU). The roadside infrastructure such as traffic lights dynamically manages the traffic in real-time by sending information or commands to the vehicles or by receiving relevant data from them. Vehicle-to-Network communication (V2N) is responsible for broadcasting global information to all cars or for streaming data to applications with a high bandwidth demand. V2N means the non-real time capable connection between a vehicle and the Internet.

Vehicle-to-Pedestrian communication (V2P) is like V2V communication except that V2P focuses on issues relating pedestrians, bicycles and other outside traffic participants [5].

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2. 5G Communication

5G is fast becoming the future of mobile communication being implemented in the ecosystem for Intelligent Transport services. 5G meets the requirements needed for effective vehicular communication in the V2X systems due to its peculiar characteristics and advantages over the current 4G LTE technology. In all the above forms of vehicular communications, there is a high demand for top notch requirements in terms of reliable, real time, low latency and high bandwidth communications. This coincides with the introduction of 5G mobile network. 5G has the capacity to provide these very characteristics. One of the use cases for 5G is based on its low-latency capabilities in supporting autonomous vehicles and V2X communications. Some improved mobile characteristics of the 5G networks include an increased performance in terms of more throughput, higher reliability, a lower latency and a higher bandwidth combined with the support for a massive number of devices. The primary target of 5G is to integrate Radio Access Technology (RAT) into the cellular system architecture. 5GAA which is the 5G Automotive Association, apparently intends to promote the use of future 5G for the development and introduction of Cellular-V2X (C-V2X). 5G technology in V2X communications brings advanced features, include fully autonomous driving by providing accurate traffic updates with low latency and large amount of data. Augmented reality by providing HighDefinition Local maps with see-through capability, Extreme mobile broadband by streaming 3D videos in a virtual reality environment. The latest LTE – V is the release 16 which is going to be come up with 5G [7]. 5G v2x communications has Radio Access Network (RAN) architecture in which cmWave macro-cellular system that coexist with LTE-based cellular system and IEEE 802.11p standards. The main role of the macro-cellular system is to provide high data rates, large distance coverage, low latency for control information which also connects with Roadside Units (RSUs). 5G is still going on research having an expert’s assumption that it may come into real world vehicular communications by 2020 [8]. The 5GAA is the clear proof of existence of cost effective and scalable access technology in C-ITS for future Autonomous transportation.

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Figure 2. Continuous V2X Technology Evolution Required [9].

The automobile industry sees two main trends with relevance for the 5G automotive vision: automated driving and road safety and traffic efficiency services.

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3. Categorization of V2X applications

5GAA [10] categorizes a comprehensive list of connected vehicles applications, categorized in four main groups of use cases:

I) safety

II) Convenience

III) Advanced Driving Assistance

IV) Vulnerable Road User.

3.1. Safety

The use cases for safety are designed to reduce the recurrence and severity of road accidents. All over the world there are researches and statistics which prove fatal loses through road accidents. These accidents are both in Vehicle-to-Vehicle and Vehicle-to-Pedestrian crash scenarios. Safety as a result accounts as an important factor in building vehicular communications.

V2X safety includes several different types of use cases to support road safety by using the vehicle-to-infrastructure (V2I) communications in terms of the provision of Real-time data analysis and data fusion in addition to the Vehicle-to-Vehicle (V2V).

One of the support services that is available to supporting vehicle users towards driving more safely and efficiently is the provision of warning applications to the driver and other roadside users. Some of these warnings include:

I) Road hazard warnings (road works, car breakdown, weather conditions, etc. ii) Intersection collision risk warning

iii) Approaching emergency vehicle warning iv) Pre-/Post-Crash

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vii) In-vehicle signage

viii) Red light violation warning ix) Traffic jam ahead warning

3.2. Convenience

Traffic management applications help share traffic information among roadway infrastructure, vehicles on the road, and centralized traffic control systems, to enable more efficient traffic flow control and maximize vehicle throughput on the road. Ultimately, these applications not only enhance traffic efficiency, but also boost the degree of convenience for drivers. Also, some other application of the convenience use case involves the provision of time-saving services to manage data and

the health of the vehicle. This requires some form of communication between the vehicle and the backend server like the vehicle OEM’s server. Some convenience-oriented applications include:

i) congested road notification ii) Free flow tolling

iii) Parking availability notification iv) Parking spot locator

3.3. Advanced Driving Assistance

The use cases here use navigation systems through Geo-positioning and digital maps to provide navigation guidance to vehicle users. They are focused on improving traffic flow, traffic signal timing, routing, variable speed limits, weather alerts and other such services to improve driving

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efficiency. Large amount of data is analyzed and computed in real time for such services to be rendered. The source of such data is provided by other vehicles, road authorities or traffic management centers.

One immense advantage of large amounts of data with high reliability and low latency would create the benefit of predictive reliability for the advanced driving use case. Predictive reliability also means that vehicles moving along should have the possibility to receive a possible prediction of the network availability ahead of them to allow preparations accordingly.

Some use cases of advanced driving assistance are:

i) Real-Time situational awareness and high Definition Maps where drivers of host vehicles are alerted ahead of time on a hazardous situation in front of them.

ii) See-Through is a use case which enables real-time exchange of live videos, images and sensor data about road situations from surrounding cameras where the host vehicle is provided a video stream of the view in front of a remote vehicle it plans to pass using the oncoming traffic lane. This situation aims to help avoid head-on collisions during such maneuver.

iii) Cooperative Lane change (CLC) of Automated vehicles is where a host vehicle signals a remote vehicle with an intention to change from its lane to the target lane of the remote vehicle.

3.4. Vulnerable Road User (VRU)

Vulnerable Road Users (VRU) are pedestrians, cyclists, and other non-vehicle road users carrying mobile devices. This use case supports a safe interaction between vehicles and these vulnerable road users. The VRUs make their location known through their mobile devices and exchange reliable and accurate information which is crucial for real-world usage. These VRUs recognizes

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vehicles in proximity and begins giving alerts to notify the drivers of vehicles and other VRU if any hazard is detected. The use of the available information provided to the vehicles is an important key element to improve traffic safety and to avoid accidents.

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4. Motivation

The driving force behind this project is the development of a warning system that ai ds the movement of emergency vehicles on our roads. It uses the underlining technology of vehicle-to-everything communication in sending and receiving messages from vehicles within a certain mile radius. With cities growing bigger and bigger, it affects the number of vehicles on our roads which effectively challenges the current road traffic management in terms of traffic congestion and traffic accidents. Road management at some point needs to meet the demands of a fast-growing society moving toward smart cities. A highly congested road is detrimental and counterproductive to the idea of an emergency system because any delay could be fatal. Emergency operations such as interventions from police, a fire rescue operation or a medical service cannot afford to be delayed because human lives and properties are literally at risk in such circumstances. As a result, the timely response and efficient operations of emergency services are of utmost importance. Also, statistics show that there is a level of emergency crashes which is not an ideal situation for services intended to help save people and property being involved in causing harm to people due to congestions on our roads. In addition to the delay and emergency crashes there is also the issue of security with unscrupulous individuals impersonating the police or the ambulance services could benefit from the road privileges given to these emergency vehicles when it comes to the right to road situation. This project seeks to address these problems faced by emergency vehicles on our roads. This is done through communication with non-emergency vehicles in an area.

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5.

Limitations and Possible Drawbacks

Cooperative intelligent transport systems are being created in order to mitigate carnage on our roads. The aim is to increase and better the driving experience of vehicle users, increase the safety on our roads and to better the overall traffic experience of all road users. This technology as described earlier is rooted in communication amongst vehicles and other road user over a wireless communication. It is in this scope that lies the possible drawback or challenge that could be potentially faced by the entities involved. Just like most gadgets and machines that communicate over a network, there is always the possible of cyber-attacks. These cyber-attacks could very much be counter productive to the aim of having a cooperative vehicle system. Persons with malicious intents who get hold of the network could potentially cause more damage through the same system that’s created to reduce the loss of life and property on our roads. The whole structure of cooperative ITS is reliant on wireless communication making it open to attacks such as denial of service attacks, eavesdropping, sybil attacks and a host of other such attacks.

In the paper [34], they discuss several potential attackers to a cooperative autonomous system. Some of these attackers are:

i) Internal Vs External – the internal attacker is obviously a known entity and very familiar with the network members whereas the external attacker is seen as an intruder coming from outside and very much unknown to the members of the network. Both attackers could offer threatening attack to the network.

ii) Malicious Vs Rational – A malicious attacker in this case seeks to destroy at all cost and would probably gain no personal gratification for their destruction. These are relatively unpredictable compared to rational attackers who seek some form of gratification from their actions and are quite predictable.

iii) Active Vs Passive – An active attacker moves to act by disrupting the system in one way or another whilst a passive attacker most of the time just eavesdrop and collects data for a probable future attack.

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iv) Local Vs Extended – The local attacker is basically limited in its scope of control. In that, they control a very small part of the network whereas the extended attacker crosses boundaries and could potentially control large number of nodes in the network.

v) Intentional Vs unintentional – the intentional attacker is a purposeful attacker who attacks on intent and with a plan of execution. They are deliberate and calculated in their attacks. The unintentional attacker is one of happenstance which could be triggered by faulty equipment.

These different types of attackers are all dangerous in several ways for the end user in an ITS. Taking control of the network could cause the loss of control of vehicles or the receipt of wrong or misrepresented data to drivers and vehicles in the system. ITS are susceptible to a host of attacks and as such in the paper [35], a list of security requirements and challenges to these requirements are made. Requirements such as:

i) Authentication - Authentication is necessary to verify the validity of a user to prevent pseudo user from gaining access to the network. Also, the source needs to be justifiable in order to trust the message coming from that source. Location validity is also needed to ensure the integrity of the information received.

ii) Data integrity – The entities in the ITS should be able to validate the data being received to prevent mis information and misrepresentation of information.

iii) Privacy – Privacy is a big deal in this era information technology and the internet. There is the need for some level of anonymity and the users of the ITS should have the luxury of deciding their privacy to the system.

iv) Availability – information availability and quickness are an important factor in ITS. Information processing in real time should be quick and available on demand.

v) Traceability and revocation – There is the need to be able to trace any malicious attackers in real time and as fast as possible in order to work towards removing them from the network and revoking their privileges.

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vi) Authorization – Just like any system, there is the need to be able to authorize the use of the system based on certain parameters. For any ITS entity, there needs to be a set of measures to deny or accept the uses and access to certain privileges in the system. vii) Data confidentiality - Attacks may happen but it is up to the system to ensure that data being processed and communicated is well hidden and encrypted to prevent any malicious attackers from getting the hands on such information.

Some of the examples of attacks in the ITS hinges on the above security requirements in the sense that if these requirements are not met there is a possibility of such attacks occurring in the system and causing much bigger problems than the whole idea of ITS is to solve, Making the system rather counterproductive. some of these attacks include:

i) Sybil attacks – This happens when the attacker assumes several pseudo identities in

order to confuse the system and to represent itself as a legitimate user of the system. A vehicle in the system would assume information coming from such nodes are legitimate and act with them accordingly [36][37].

ii) Jamming attacks – Jamming is done by transmitting noisy signals with high

frequencies in order to cause an interference in the system. This causes the vehicles and other entity nodes to be unable to communicate with each other [38][39]. iii) Flooding attacks – The system is flooded with irrelevant messages to the point where

the important messages are lost in the pile of unimportant messages. Since the ITS deals with time in the prevention of road accidents, this could cause an unnecessary delay in the transmission of vital information [40].

iv) Malware attacks – This is basically using malware such as viruses, worms and trojan

horses to attack the network and the software component of the system entities in order to disrupt their regular functioning [41].

v) Black hole attack – This is a situation where a malicious node pretends to be a part of the system but fails to participate in information transmission deliberately. This could cause grave problems for the vehicles and other components in the system [41][42]. vi) Falsified entities attack – In this scenario, an attacker obtains a valid identification of

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roadside unit could be mimicked and used in causing huge disruption to the vehicles and other road user in the system [42][45].

vii) GNSS spoofing and injection attacks – GNSS is Global Navigation Satellite Systems and

is used in providing location information to the ITS. Accurate and authentic location information is vital to the ITS and any compromise of it is a real danger to the cooperative ITS. Giving wrong positions to neighboring vehicles could cause serious damage to life and property [46][47].

viii) Masquerading attacks – The attacker in this instance hides under a valid identity and disseminates false information to the neighboring vehicles for its own malicious intent [48].

ix) Data alteration attacks – This attack tries to modify or delete the content of data being exchanged in the system. It calls the integrity of the message transmitted into disrepute causing problems to the receiving vehicles [49][50].

x) Eavesdropping attack - This type of attack is passive and does not necessarily affect the network or its entities. The basic attack is to extract some interested sensitive information from the network. The privacy of the users in the system is what is affected in this case [46].

There are many more possible attacks that could happen in the cooperative intelligent transport system (ITS). The aim of this chapter is not to profess solution to such attacks but to inform the reader of such possibilities in the ITS. Most of these attacks are purely based on theoretical analysis as there aren’t that enough researches on the practicality of such attacks. But as the ides of autonomous vehicle keeps growing, there would be a certainty in the analysis in the coming years.

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6. Related Work and Publications

There have been many articles and publications that have discussed various forms of the subject matter of this project. In the article [7], which is the white paper on automotive vertical sectors, the focus is mainly on the collection of data which could be used to improve eBusiness, to enhance the digitalization of transport and logistics. The idea is to realize new business potentials from the advancement of the automotive industry with the introduction of 5G in the industry. Big data has become an important factor in the creation of services across the globe and the possible services from the automotive industry is no exception. The Internet of Things (IoT) will aid in collecting additional data which will complement the data collected from vehicles, roadside components and the traffic management centers. The data collected could help facilitate the creation of services in the business world. In this article, they describe certain key transformations happening in the automotive industry. The transformations are 1) Automated Driving, 2) Road Safety and Traffic Efficiency Services, 3) Digitalization and Transport Log istics, 4) Intelligent Navigation and 5) Information Society on the Road.

On the transformation of Automated Driving, he US Society of Automotive Engineers (SAE) and the German Association of the Automotive Industry (VDA) have prescribed six levels of automation which moves from level zero to level five. The levels include i) no automation ii) Assisted iii) Partial Automation iv) Conditional Automation v) High Automation and vi) Full Automation. These levels show a projection of what the automotive industry seeks to achieve in the future of self-driving cars.

The Road safety and Traffic Efficiency Services describes the potential usage of vehicle-to-Everything communication. Communications enable the provision of warning notifications for the vehicles on the road. These warning are generated from the communication of the vehicle with other components of the vehicle-to-everything communication ecosystem. Some of these warnings include Intersection Collision Risk Warning, Road hazard warnings (road works, car breakdown, weather conditions, etc.),

Approaching emergency vehicle warning, Traffic jam ahead warning etc. These are to protect the safety of the various road users and to ensure the efficient use of traffic on the road.

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In the Digital and Transport Logistics transformation, the Directorate-General for Mobility and Transport (DG MOVE) presented at the first Plenary of the Digital Transport and Logistics Forum (DTLF) four “content drivers” concerning the digitalization of transport: harness and use data increasing the efficiency of the transport logistics chain, decarbonization, human factor and international aspects. The idea of these four content drivers are centered around the da ta and information relating to goods, means of transport, authentication and access to ports or customs clearance information. The exploitation of the data collected will play a significant role in the logistics chain. There are different advantages that the transport and logistics industry stand to gain with the use of Intelligent Transport Services. The possibility of an increase in road transport efficiency and route optimization which could significantly reduce travel times and thereby reducing the consumption of energy. The data collected also helps in the sharing of large amount of useful information to the supply chain actors. Provision of real time information will also be useful in the track and trace of goods across borders increasing the efficiency of a global logistics chain.

The Directorate-General for Mobility and Transport (DG MOVE) presented at the first Plenary of the Digital Transport and Logistics Forum (DTLF) four “content drivers” concerning the digitalization of transport: harness and use data increasing the efficiency of the transport logistics chain, decarbonization, human factor and international aspects.

Intelligent Navigation deals with Navigation systems which provide geo-positioning and digital maps as navigation guidance to drivers. Navigation systems provide drivers with optimized route with the data collected for the road users and vehicles. These routes are provided in real time to drivers to ensure the efficiency of the road user experience. Also, with an increase in useful data, there arises the provision of additional user services that can be rendered by the navigation systems. Services such as providing information of certain key hospitality services like restaurants and hotels in a geographic area. Also, locations of banks and post offices is rendered due to the big data available to the navigation system.

Information society on the road provides a connectivity performance that will make vehicles have the potential of substituting the home or the workplace. With the automated driving in place to

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happen soon, the drivers of such vehicles will be at liberty to work or relax in the scenario of a partial or full automation of the vehicle.

This paper also provides new business models that could be of interest to companies that have a direct or indirect business model relating to humans and vehicular interactions. The three business models described are i) Pay as you drive ii) Mobility as a service iii) Predictive Maintenance.

Pay as you drive services is a service which will be of interest to insurance companies and vehicle rentals as it seeks to determine driving service cost according to vehicle usage. The evaluation of the actual distance covered together with the environment the vehicle has been used will create a better case for the calculation of risk. Collecting various types of data accurately is necessary to properly evaluate risks and assess driving costs. This way the industries that rely on vehicle risk will have an accurate measure on how to progress.

With the various modes of transport, whether private or public, the knowledge of choice could be better informed in Mobility as a Service as users make decisions of mobility based on a variety of factors in real time. Users could find the appropriate means of transport, with the best conditions and actual need of such transportation. This could save time and money as well as keep people safe.

Predictive maintenance enabled through data captured from sensors and predictive analytics will help vendors and Original Equipment Manufacturers (OEMs) to offer long-term service based mobility solutions to customers. Manufacturers with the help of big data can now be able to monitor and predicts faults before they happen to help save cost and human lives whilst keeping machinery on high productivity. With this level of information analysis, business can move beyond just sale of product to long-term maintenance, repair and customer support.

In this paper, there is an emphasis on the development of new business models through the automotive industry and the telecommunication industry with respect to the advancement of 5G networks and the advantages it provides in communications between vehicles and everything.

Most papers and articles on automated vehicle communications under Intelligent Transport System usually research on the vehicles and the drivers of these vehicles. But this paper [11]

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focuses on the implementation of a MEC-based Vulnerable Road User Warning System. The paper overviews the V2X communications but zones in on Advanced Driving Assistance (ADA) and Vulnerable Road User (VRU). The warning system is applied to the VRUs like pedestrians, cyclists and other vehicles. These VRUs are notified of any impending dangers or any road related impediment through Cooperative Awareness Messages CAM on their mobile devices. The architecture is designed in a way that the user side application periodically sends the position, speed and orientation of the VRU. A Multi-Access Edge Computing application processes this information to generate the geographical location of the VRU. Once the position of the VRU position is known, it can predict if there could be a collision with an oncoming vehicle or not. The VRU warning system is basically an exchange of information between road users. The application of this is highly important in the era of massive attention and focus on our mobile devices or smart phones. There are multitudes of road users who have their attentions off the road at critical times, listening to music, surfing on Internet, or texting. This paper seeks to reduce the rate of collisions and road user accidents by being able to leverage on the devices that catch our attentions in solving the problems of VRU accidents. With 5G technologies on the rise and the availability of cloud and Edge based computations, it is very possible to send warning notifications to road users who otherwise would have their heads in their screens or headphones on rather than watching the road for any impending dangers.

The paper discusses pedestrian road safety in two different ways. These are done through the passive and active mechanisms [12]. The passive mechanisms consist of educating the public on road usage, creation of clear road signs, visibility on the road, car modeling etc. These are carried out to ensure the reduction of road accidents caused by some of these factors. The other mechanisms are rather active, and infrastructure based. They rely on sensor based or communication-based solutions [13] [14]. These infrastructures could be sensors, cameras, radio tags, Roadside Units (RSU), or GPS equipped smart phones [15]. These gadgets are used to aid in identifying and detecting the presence of VRU. With wireless communications, these gadgets connect and communicate and that helps to reduce road accidents [16].

The implementation of a MEC-based Vulnerable Road User Warning System as described in this paper relies on the vehicle-to-everything communication. In the ecosystem of automotive

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communications, vehicle-to-Pedestrian and vehicle-to-Infrastructure are the forms of communications that are very much utilized in this project. This is how notifications are sent to alert the motorists about the presence of VRU and alerts VRU about approaching vehicles. The final part of the paper measures an experimental performance evaluation and compares the latency of the messages as experienced by the users or client. The two different means of computing are based on the Multi-Access Edge Computing based CAM Server and a Cloud-based Cam Server. It is realized that the Edge based CAM Server has a lower latency as compared to the Cloud based Server.

This white paper on Edge computing for advanced automotive communications provide an overview of some use cases of on the usability of edge computing [10]. Edge computing refers to locating applications and the general-purpose compute, storage, and associated switching and control functions needed to run them-relatively close to end users and/or IoT endpoints [17]. In this context, Edge computing is used to provide compute, storage and networking capabilities at the edge of the network to supporting multiple services for connected Autonomous Driving Vehicles. Multi-access Edge Computing (MEC) as a standardized solution for Edge Computing. With regards to the automotive industry and the various known types of vehicular communication, Edge Computing brings to the fore one type of vehicular communication which is the Vehicle-to-Cloud communication. This presents the cloud as a form of infrastructure in the vehicle communication ecosystem. The solutions made available in this paper regarding the Vehicle-to-Cloud communications create edge cloud capabilities for two levels of autonomous driving. These autonomous driving include Highly autonomous Driving (HAD) and Fully Autonomous Driving (FAD). Highly Autonomous Driving (HAD) is the third stage of autonomous driving which gives drivers more freedom to completely turn their attention away from the road under certain conditions. In other words, they will be able to hand over complete control to the car. Whilst Fully Autonomous Driving (FAD) is the fourth level of autonomous driving in which the Vehicle can handle most driving situations independently. The technology is developed to the point that a vehicle can handle highly complex urban driving situations, such as the sudden

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appearance of construction sites, without any driver intervention [18]. The Edge Computing based Vehicle-to-Cloud solutions are able to process and provide geographical maps in real time and in high definition, traffic notifications based on the monitoring of traffic, road hazard warnings and a host of other traffic messages that could help for a better driver traffic experience. For fully autonomous driving, 5G networks will play a major role in curbing any challenges that arise and a such very necessary for this level of autonomous driving.

One of the main reasons for a transition from the cloud to the Edge Computing for connected Autonomous Driving services is as a result of the need a more processing power closer to the vehicle. The closer the technology is closer to the vehicle the lower the latency and the need to have reduced network churn with continuous access to the cloud. The Edge Computing due to its proximity to the vehicles and Roadside Units offers a different type of cloud computing capabilities without the baggage of Cloud computing in terms of latency and processing power. With increasing volume of data being accessed from vehicles and roadside units, Edge computing seems very much suited to handle processing, computing and storing of such data based on the needs of the individual units.

According to the paper, these features are found to be beneficial to Edge Computing:

i) Network Slicing to tailor the capacity and capabilities of the network for each different service. ii) Service-specific profiles for dynamic assignment of service-specific HW-acceleration to optimize the compute and storage based on simultaneous services requirements.

iii) Hierarchical deployment of the Edge Computing environment using a hierarchy of gateways/roadside units with the Edge Computing servers arranged to reduce the latency and distribute the processing.

The capabilities of Edge Computing have the potential to open a multitude of business opportunities. The Mobile Network Operators and other service providers will be able to leverage on the vast pool of connected resources of Edge Computing to Autonomous Driving vehicles. This kind of infrastructure can lead to new business opportunities such in a variety of business scenarios which include 1) Business-to-Business (B2B) 2) Business-to-Consumer (B2C) 3) Business-to-Business-to-Consumer (B2B2C) 4) Consumer-to-Consumer (C2C).

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The white paper provided an overview of the role and link between Edge Computing and the automotive industry. Stipulating why there’s a need to move from cloud to Edge computing and showing how Edge Computing can be considered as a key technology supporting multiple services for connected AD vehicles [10].

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7. Problem Description and Objectives

The ideal road situation in an emergency transportation system will be the ability for emergency vehicle to move through towns and cities without any resistance from traffic congestion and without causing any traffic engagements along the way. This is an ideal situation and not very much applicable in most places across the globe. With large cities and towns comes a higher population and increased business activities which result in greater demand for cars and vehicles for transportation. This creates the need for a larger pool of emergency service vehicle to care for the needs of the growing population as well. So many vehicles in increasing number on mostly an already old existing road infrastructure, there is the need for a solution. Even with new road infrastructure, the protocols available to road users, does very little to limit the interactions which lead to congestions on our roads. This congestion causes a problem for most road users and in our case most especially, emergency vehicles. Modernization, migration, and globaliza tion have also taken great tolls on road usage. Inadequacy in transportation infrastructures can cripple a nation’s progress, social well-being, and economy [19]. On a general scale on the issue of congestion, there are so many reasons why a congested city in terms of traffic systems can cause so much disadvantage to such a city or town. The very small issue of delay has a list of damaging effects to a community which include pollution, road rage and a host of other undesirable effects. The idea of building efficient highways and roads haven’t been able to completely solve the problem of increasing congestion in cities. Also, the building of faster vehicles to surmount greater distances have not entirely helped in the situation. The advent of the Internet and the IoT introduced the embedding of sensors and advanced electronics, making vehicles more intelligent, sensitive and safe to drive on. This was a step towards building something entirely different from before to aid in traffic management. Now with advancement in communication networks, transportation can be coordinated in a much rather synchronized manner to allow for batter traffic movements. These innovations in wireless mobile communications and networking technologies are starting to impact cars, roads, and highways. This impact is drastically changing the way we view transportation systems in this generation and will greatly influence the way we drive in the future. It will create major economic, social, and global impact through the

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transformation over the next period of 10–15 years. In this project, we focus on emergency vehicles taking advantage of this change to affect its movement on the road in relation to other users on the road. The effects of congestion on emergency vehicles moves past pollution or economic effects but can very much cause the loss of life, hence why its emergency.

The objective of this project is to highlight the role of emergency service transport in the larger context of road usage and road safety and how we can be able to leverage the current vehicular communication technologies over the 5G communication network. The key objectives of this project include but not limited to i) achieving minimum response time for emergency services, ii) achieving minimum disruption of the regular road traffic flow, and iii) satisfying the security requirements of the road network authorities [20]. Emergency vehicles should be able to communicate to other road users of their presence some minutes before they are present for other road users to take actions necessary for a safe and free passage for the emergency vehicle. Also, with communications, the vehicles including emergency vehicles will be adequately informed of any impediments in front of them or on their chosen route. Incidence like road blockage due to accidents or faulty vehicles or any environmental causes. This enables the emergency vehicles to be able to reroute to take a much safer route. This goes a long way in saving time which is one of the main factors to consider in the emergency service world. The safety and timeliness of emergency vehicles save lives and property. In this project we implement one way in which vehicles can communicate to the advantage of emergency vehicles.

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8. Road Safety & Emergency Services

Figure 3. Various aspects of road safety [21]

As shown in Figure 3, Driver and Passenger Health involves multiple factors. It involves understanding road conditions, providing enough response time to emergencies, procedures for accident prevention and many more. Generally, it is agreed that the exchange of relevant safety information through V2V and V2I communications will achieve increased road safety. Where warning information is either provided to the driver or activated by the active safety systems. A collision warning system on a vehicle needs to know the trajectories of neighboring vehicles and the configuration of the neighboring roadway. Previously, most collision warning systems study the state of the neighborhood by using sensors like radar or laser vision systems. In contrast, the modern Cooperative Collision Warning (CCW) systems will construct their knowledge of the neighborhood by communication with the other vehicles through the availability of fast communication networks that 5G technology provides. This has the advantage of a potentially

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inexpensive complement of on-board vehicle equipment, as well as providing information from vehicles that may be blind to the direct line of sight to the approaching vehicle [22].

Examples of CCW applications are:

(a) Forward Collision Warning (FCW), where a host vehicle uses messages from the immediate forward vehicle in the same lane to avoid forward collisions.

(b) Lane Change Assistance (LCA), where a host vehicle uses messages from the adjacent vehicle in a neighboring lane to assess unsafe lane changes.

(c) Electronic Emergency Brake Light (EEBL), where a host vehicle uses messages to determine if one, or more, leading vehicles in the same lane is braking [23].

Cooperative Driving in the context of vehicle communication allows drivers to exchange traffic information to reduce the occurrence of traffic delays, decrease CO2 emissions and avoid road

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accidents. This provides the perfect scenario for the existence of emergency vehicles as a tremendous support for transportation. City authorities could also be supported through different security measures with the information collected, both on vehicles and road conditions.

In figure above, there is a depiction of the ecosystem of communication between various road user. There is a direct communication between vehicles, communication between vehicles and roadside units, and communication between vehicles and roadside infrastructure. These communications are facilitated by the cellular communication infrastructure available which also connects to a remote-control unit. The emergency transport services will have this system to facilitate communication between it and the surroundings.

Figure 5. Inter communication between vehicles.

This figure also shows a much-simplified view of communications between different types of vehicles. The roadside units are connected of the Internet and the Intelligent Transport Service Platform. The ITS platform could be a CAM (Cooperative Awareness Messages) server or any other server of such kind that relays messages across the different units on the road.

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Figure 6. Use case for emergency vehicle communication [25].

The figure above depicts our exact scenario in terms of leveraging on the ecosystem of vehicle communication to aid in the movement of emergency vehicle. In this picture, all vehicles on the road are giving information about their geographical location and other such attri butes to the network server for calculation and use of such data. With apt knowledge of the location of the various vehicles are, there is the ability of the server to relay such information to the emergency vehicle and vice-versa to awareness to be created on our roads as quick as possible with the help of 5G communication networks [25].

The continuous back and forth communication has the potential to drastically reduce the chaos on the road from the point of view of the emergency vehicle driver.

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9. Mobile Edge Computing in V2X Communications

In this era of 5G, huge amount of data will be created and one of the very important use cases will be in the field of vehicular communication as discussed thus far in this report. With big data comes the need for huge computing power and faster infrastructure to reduce the delay of relaying information. In the matter of vehicular communication, the high growth in number of sensors in connected vehicles and roadside infrastructure calls for the efficient and effective large data processing and storage. Using the cloud is not enough as most of the applications that would be deployed will need a low latency and high bandwidth information process. As a result of this, the MEC (Mobile Edge Computing) which is a promising new platform or big data processing and computing resources with low cost and reliable storage. It has the potential to save network resources by getting cloud sources closer to the vehicles and various data sources to reduce latency [26].

Edge computing provides cloud computing capabilities at the edge of the network. Mobile Edge Computing is one of the key supporting technology in 5G V2X communications. This provides proximity, high bandwidth, ultra-low latency, real time radio access networks and effective location awareness to avoid collisions. MEC enables direct communication between vehicles as well as between vehicles and cloud through cellular networks where vehicles can communicate with very low latency and high bandwidth. Edge Computing is addresses the issue of the need to have more processing power at the edge of the network closer to the vehicles to ensure the availability of reduced latency by aiming to offer a different service and cloud-computing capabilities within the roads infrastructure and the access network infrastructure in close proximity to vehicles and Road Side Units (RSUs) [26]. The MEC application acts as an RSU for V2I communications. It recognizes road hazards and gets alerts, as it sends information to the nearby vehicles with extreme low latency [27].

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Figure 7, showing an example of Edge Computing Support to Services for Connected and Autonomous Vehicles [10].

Network Slicing is the tailoring of the capabilities and capacity of the network for each different service. This ability is available to MEC as the figure above shows. Also, another advantageous feature of edge computing is the ability to be able Service-specific profiles for dynamic assignment of service-specific Hardware-acceleration to optimize the compute and storage based on simultaneous services requirements.

Due to the compatibility of the 5G network in terms of network slicing, there is the ability to support different services running across a single radio access network. This is depicted in the figure below. It shows how Edge Computing brings cloud capabilities close to the vehicles and sources through the concept of network slicing [27].

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Figure 8. a cloud-centric one-to-many architecture to edge computing [28].

Moving from a cloud-centric one-to-many architecture to edge computing can reduce traffic and latency and improve power performance [28]

There are a multitude of very exciting and interesting applications that arise due to the power of MEC. The information exchange between vehicles, infrastructure, pedestrians, and network using V2X technology opens a world of possibilities of which this project is rooted on. The completeness of such possibilities makes the aim of safety and speed on the part of emergency vehicles a possibility. The MEC makes available some very important use cases in the area of vehicular communication. Exploitation of the edge processing power provides a lot of useful and exciting low latency service experience. The Edge Computing provides cloud-centric computing capabilities at the very edge of the mobile network. This provides very low latency between the client and the server applications, high bandwidth for the application traffic, and near real -time access of the applications to context-rich information. It is in line with such attributes that the 5GAA (5G Automotive Association) has categorized a list of automated vehicle applications into four main groups. These groups are safety, convenience, Advanced Driving Assistance and

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Vulnerable Road User. These application groups have use cases that rest on the computing capabilities of Edge Computing. These use cases are tremendous in achieving the aims of a safer and faster emergency transport service with little to no impediments

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10. Use Cases on Capabilities of Edge Computing

1) Real-Time Situational Awareness & High Definition Maps

Figure 9. Real-time situational awareness and high definition maps [10].

The situation in this case is when real time information on the condition of a road ahead is communicated by one vehicle to another oncoming vehicle. The driver of a host vehicle is alerted on a potentially hazardous condition ahead of them. Due to the real-time and local nature of the information needed for accurate and augmented situational awareness of the road users, Edge computing is aptly ideal in this situation [26][30].

2) See- Through for Passing

Figure 10. Passing see through [10].

Like the name suggests, this is a passing or overtaking aid to drivers on highways. A video stream of the view of a vehicle ahead is shown to the vehicle behind to well inform them of the situation

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ahead in a case of an attempted over-taking. Driver of Host Vehicle that signals an intention to pass a Remote Vehicle (RV) using the oncoming traffic lane is provided a video stream showing the view in front of the RV. This use case is especially important as it attempts to prevent head-on collisihead-ons head-on our roads. Edge Computing is an ideal solutihead-on for use cases like See-Through where very low latency communication and local context are key characteristics. Also, for the host vehicle to safely over-take the remote vehicle in this scenario, there will be the need for a lot of computations to be done in relation to the distance of the remote vehicles and the speed at which they are moving. The computational power of the MEC for the data processing will be highly needed [26][30].

3) Vulnerable Road-User Discovery

Figure 11. Scenario for vulnerable road user [10].

An innocent roadside user can be saved from a crash from an oncoming vehicle if data collected in put into use. For the host vehicle to know of the roadside user in its blind spot, it will need to calculate the trajectories of the host vehicle and vulnerable road user, know the geometry of the road and intersection and determine the risk of collision in the time frame where their trajectories meet. The application then issues a warning message informing the user of the impending danger.

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Considering the time and the data processing that will take place, it is reasonable that MEC is ideal to handle such a situation [26][30].

The mobile edge computing solves a lot of issues in the area of vehicle communication and has the potential of opening a world of vast possibilities to us. Handling of big data from roadside infrastructure and vehicles only makes sense if the processing power and storage capabilities are brought closer to the vehicles and to the other data sources from the Cloud thereby saving network resources and reducing latency [26].

In the wireless communication technology, there is the issue of multi-operator support. Different road users and vehicles will use different services provided by different operators. Cross-operator interoperability is therefore critical for V2X applications enabled in the edge cloud. MEC enables the Cross-operator interoperability for V2X communications. In the multi-operator scenario, the location of peers for different traffic between the mobile operators should limit the end-to-end latency between the vehicles. If MEC is availably close to the peers, then the end-to-end latency will be the same. To achieve low latency, new local peering points between the mobile operators’ networks need to be deployed [28].

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11. Technical Use Cases for V2X

The intersection of the automotive industry and the communication industry is what has enabled the development of the V2X communication as we have it now and the continuous collaboration is what is going to enable the development of future technologies in this regard. For there to be a fast and successful path towards a more efficient and safer driving experience in the future, there needs to be clarity on some use cases and requirements on the technology. In this section, we will attempt to explore some use cases, requirements and key performance indicators (KPIs) in the area of both the automotive sector as well as the communication and networking sector considering the rise in 5G and its use in this fusion. The automotive KPIs describe the behavior or services for road users whilst the communication KPIs describe the requirements for information exchange between road users and between the road user and the infrastructure. The two sets of KPIs are connected and correlate with each other.

According to the 5GCAR project [29], it introduces five use case classes that has been identified as the relevant classes for the considered time frame and scope. These five use cases are:

1) Cooperative maneuver, 2) Cooperative perception, 3) Cooperative safety,

4) Autonomous navigation, and 5) Remote driving.

In these five use case classes, we are going to describe one use case, the requirements for the use case as well as the key performance indicator (KPIs) of the use case.

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11.1. Cooperative maneuver

Cooperative maneuver is the situation where vehicles share local awareness and driving intentions with the vehicles in its local environment and to other road users in that geographical location via wireless communication. The information shared could lead to a potential coordination among these vehicles. With coordination among the vehicles and the awareness of the driving trajectories of each other, there could be an improvement in the decision making of the drivers which could directly lead to traffic safety. Also, the road traffic management could increase in efficiency.

One important use case in this situation is lane merge. Lane merge is when a vehicle from another lane of the road intends to merge onto a main road as depicted in fig. below. In the figure below, one vehicle intends on merging onto the main road. The vehicle making the merge could share its local awareness with the vehicle on the main road as well as its driving intentions. The information given by both vehicles could be computed and the driving trajectories and speeds of both vehicles adjusted accordingly to allow for a rather smooth, safe and more efficient lane merge. The requirement needed for the connected vehicle is a wireless communication capability and a Global Navigation Satellite System (GNSS) [8][29].

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11.2. Cooperative perception

Cooperative perception is the exchange of information from various sources. Sources such as radars, laser sensors and stereo-vision sensors from on-board cameras. Information gathered is shared among vehicles and infrastructure as well as vulnerable road users in the local area. The idea is to merge the locally gathered information with remote information from a remote server. The goal of this is to extend the local perception range of the line-of sight and local field-of-view of an individual vehicle. This will help to reduce traffic accidents caused by the limited view of drivers. Driving maneuvers such as overtaking or lane changing can be done with much efficiency as the drivers will be able to detect oncoming traffic in time to make important decisions. Also, they would be able to avoid hidden or sudden obstacles.

One important use case is the see-through use case which has been explained above in fig.(see-through). In this case, the exchange of video between vehicles via wireless communication is essential to the safety of drivers trying to make an over-taking. With the help of the vehicle in-front capturing and sending a live feed to the vehicle behind, it enables the rear vehicl e to see through the vehicle in-front and to make an informed and safer decision on the maneuver it intends to take [8] [29].

The vehicles in this scenario will need a vision-based sensor like a stereo camera and a computational server which could be the MEC, to run the corresponding computer vision processing part required for the application.

11.3. Cooperative Safety

The principle of cooperative safety deals with the exchange of information for the detection of VRUs (vulnerable road users) on the roads. With the advancement of IoTs, almost everybody owns a smart-phone and as a result, the best method of detection will be through wireless cellular communication in the age of 5G. Also, cameras and sensors stationed on some road infrastructure

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could be of use. Mainly though, the vehicle to pedestrian communication model will be employed for the detection. The available VRU information can be exchanged between all relevant users and infrastructure entities. Alert messages can be sent to the VRU or to the vehicles upon detection. Such messages could cause a vehicle to reduce its speed or apply it breaks to help protect the VRU. This is to ensure a complete provision of safety to the surroundings of the vehicles. The VRUs include the pedestrians, cyclist, motorcyclist and pets. One use case of this is the Network assisted vulnerable pedestrian protection [8] [29].

Figure 13. Vulnerable pedestrian protection assisted by the network [29].

This use case is primarily focused on non-vehicular entities that also use the road. Entities like pedestrians who are close to the road or trying to cross the road as depicted in fig(above). With the exchange of information between the vehicle and the road user through his smart device, the vehicle can detect the presence of the pedestrian and act accordingly to ensure safety of the pedestrian.

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11.4. Autonomous Navigation

The aggregation of collected information will help in building Real-time Intelligent High Definition (HD) Map with very precise context information such as road structures and reference objects for localization. This will help in the navigation of optimal routes by semi or fully autonomous vehicles. A save of energy and time for optimized routing decisions will be achieved by using an HD dynamic map. The use case for autonomous navigation is High definition local map acquisition.

The gathering of information from different sources, from the map provider to the different road users available in order to create an optimal route map as depicted in fig(below). The goal is to update the local dynamic map of vehicles on the move [8][29].

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11.5. Remote Driving

Remote driving like the name suggests is being able to control the various components of a car from a remote position via wireless communication. For there to be safety in remote driving, there needs to be information about the perception layer which include vehicle sensors and maps and information about the infrastructure of the area. This is to mainly provide convenience for drivers and vehicle occupants from driving in a safe and efficient manner. The use case for this is Remote driving for automated parking [8][29].

Figure 15. Remote driving and automated parking

In this scenario, the cloud or MEC provides the vehicle with the appropriate driving trajectories which is gathered from the cameras and sensors as well as communication with other infrastructure available in the parking facility. A vehicle to network communication can be applied in this regard.

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12. Technical Requirements and KPIs for V2X

End-to-end Latency (ms) – This is the total time elapsed by the transmission of a data packet from source to destination across a network.

Reliability (10-x) – This is the maximum packet loss rate that can be tolerated by the network. When transmission is not fully received by the destination, the is a loss of packet and that affect the reliability.

Data rate (Mbit/s) – This is the number of bits sent per unit time in a network. The rate at which data is sent through the network.

Communication range (m) – This is the maximum distance between a source being the transmitter and a destination being the receiver with the application reaching accepted reliability.

Node mobility (km/h) – this is the relative speed and directive at which an object is moving in order to achieve the specified reliability.

Network Density (vehicles/km2) – This is the maximum number of vehicles at a point in time per unit area

Position Accuracy – Localization is needed for geographical position accuracy. The local area of contention.

Security – This entails privacy and the authentication of data. The ability to use features to protect user authentication, the authenticity of data and the integrity of data and the people privy to that data.

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Cost – this is the expense realized as a result of the additions of new technologies and components in newly created scenarios.

Power Consumption – The energy used is an important requirement from different points of view. From the view of vehicles and the consumption of power and from the view of infrastructure and other users consuming and using energy.

Availability – Availability is the need to have a service readily accessible upon request. Often, there is a trade-off in the system on the issue of availability and reliability. A service readily available might not necessarily make it reliable and vice versa.

Intersection Crossing time – The minimum and the maximum time it takes for crossing intersections on the road.

Maneuver Completion Time – The total time it takes from the start of a road maneuver to when it is completed.

Car Spacing – The required or recommended distance to be observed between to vehicles in a location.

Takeover Time - The maximum time to determine the completion of a takeover.

Area of Relevance - Relevance area is the distance and traffic direction where the messages must be distributed to ensure the automotive service [7].

These are the definitions of some of the requirements needed in the automotive and communication space with regards to the use cases and their key performance indicators. The following requirements and KPIs will be divided into the various use cases as explained above and described in 5GCAR/D2.1 documentation [5GCAR] [29].

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The respective KPIs according to the 5GCAR use cases are: 1) Lane merge: Localization, Latency

2) See-through: Data rate

3) Network assisted vulnerable pedestrian protection: Reliability, Localization 4) High definition local map acquisition: Localization, Density, Security

5) Remote driving for automated parking: Availability, Reliability, Latency[5gcar][29]. Conditions under which the latency and reliability figures should be achieved[5g -ppp][7].

Table 1

USE CASE 1: Lane Merge

Requirement Label Requirement Value and Requirement Unit

Latency < 30 ms

Reliability 99.9%

Availability V2I/V2N/V2V 99%

Data Rate 0.350 to 6.4 Mbps

Communication Range > 350 meters

Relevance Area 250 to 350 meters

Power Consumption Low

Cost Medium

Position Accuracy 1 to 4 meters

Car Spacing 0.9 to 2 seconds

Mobility 0 to 150 km/h

Take-Over Time 10 seconds

Maneuver Completion Time 4 seconds

Security Privacy: High

Confidentiality: Low Integrity: High Authentication: High

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USE CASE 2: See - Through

Requirement Label Requirement Value and Requirement Unit

Latency 50 ms

Reliability 99%

Availability 99%

Data Rate 15 to 29 Mbps

Communication Range 50 to 100 meters

Relevance Area 300 to 500 meters

Power Consumption Low

Cost Medium

Position Accuracy 10 meters

Car Spacing 0.9 seconds

Mobility 0 to 30 km/h

Take-Over Time 4 seconds

Maneuver Completion Time 4 seconds

Security Privacy: Medium

Confidentiality: Low Integrity: High Authentication: High

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