The Forerunner UAV Concept for the Increased Safety of First

Responders

Mih

aly Nagy

1,2

, P

eter Bauer

1,2 a

, Antal Hiba

1,2 b

, Attila G

ati

1,2

, Istv

an Drot

, Bal

azs Lattes

and

am Kisari

1,2

Research Center of Vehicle Industry, Sz

echenyi Istv

an University, Egyetem t

er 1, Gy

or, Hungary

Institute for Computer Science and Control, ELKH, Budapest, Hungary

National Media and Infocommunications Authority, Budapest, Hungary

Keywords:

Forerunner UAV, First Responders, Basic Concepts, Wireless Communication.

Abstract:

This paper proposes a novel Forerunner UAV concept to increase the safety of ﬁrst responders by monitoring

the road in front of their emergency ground vehicle (EGV) and notifying the driver about any violation of

his/her right of way or approaching danger. The developments are conducted in an R&D project in Hungary.

The proposed UAV for the planned urban demonstration is a hexacopter with triple redundant architecture

applying a gimbaled camera to monitor the surroundings. In the cooperative control of the EGV and UAV

the UAV must ﬂy in front of the EGV which is possible through wireless communication of route data and

velocity. Besides the real system a computer simulation representation is also applied including CARLA and

Matlab to make exhaustive tests of the system capabilities. Increased attention is devoted to the possible

wireless communication solutions as these are safety critical parts of the system. The article ends with the lists

of planned simulation and real test scenarios to evaluate the system.

1 INTRODUCTION

Considering the overview works and statistics in

the US a major cause of the injury and death of

ﬁrst responders besides injuries and deaths in action

is due to trafﬁc incidents (crashes) see (Casey

C. Grant and Merriﬁeld, 2011), (Donoughe et al.,

2012), (NHTSA, 2014), (Smith, 2015). Unfor-

tunately we could not ﬁnd any EU statistics but

accidents involving ﬁrst responders regularly happen

in Hungary. In May 2019 there was a crash in

the US (luckily with only minor injuries) caught

on video between a train and a police car (CNN,

2019) (https://edition.cnn.com/videos/us/2019/05/

22/train-hits-police-cruiser-newsource-orig.cnn)

because the police car did not see the approaching

train behind the other. An aerial view of the situation

could help the driver to avoid this accident as possibly

could help in several situations when the approaching

vehicle is covered from the driver. This has led us

to the idea to propose a Forerunner UAV (unmanned

aerial vehicle) which can ﬂy in front of the emergency

https://orcid.org/0000-0002-1925-2270

https://orcid.org/0000-0002-5706-3550

ground vehicle (EGV) and monitor the road / area to

detect approaching vehicles and vehicles failing to

give the right of way. In case of hazard detection, the

driver should decrease speed and handle the situation.

Beyond risk alert, the minimum requirement is to

stream the aerial view to the driver of the EGV

to increase his/her situational awareness. Further

improvements can be to colour the area in front of

the vehicle (green = free route, red = danger, not

enough space to pass) and/or denote the threatening

vehicle(s) with red colour.

To the best of our knowledge, this Forerun-

ner UAV name has not been used in this meaning,

searching for forerunner drone/uav/aircraft/helicopter

in Google Chrome the best hits are:

• The forerunner position of a drone company (In-

side Unmanned Systems, 2019)

• A computer company (Forerunner Computer Sys-

tems, 2020)

• A computer game character (fandom.com, 2020)

• A sportwatch (Garmin, 2020)

so the Forerunner UAV name (and the concept) can

be considered completely new. This year (2020) a re-

362

Nagy, M., Bauer, P., Hiba, A., Gáti, A., Drotár, I., Lattes, B. and Kisari, Á.

The Forerunner UAV Concept for the Increased Safety of First Responders.

DOI: 10.5220/0010408203620369

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 362-369

ISBN: 978-989-758-513-5

search project is started in Hungary to evaluate the

feasibility of the Forerunner UAV concept and this

article is the ﬁrst publication summarizing the initial

ﬁndings of the development.

Considering the literature about following and/or

supporting a ground vehicle (GV) by an aerial vehi-

cle several solutions can be found. First, there are

solutions helping the remote operator of the GV with

the aerial images taken by the UAV on demand of the

operator (Cantelli et al., 2013) or with continuously

streamed video (Harik et al., 2015). Second, solutions

utilizing aerial images in pre-planning of GV route

(Zoto et al., 2020) can be found. Third, there are so-

lutions on-line supporting the unmanned ground ve-

hicle (UGV) in path planning by processed informa-

tion from the UAV images (free paths, obstacles etc.)

(Lakas et al., 2018), (Peterson et al., 2018). Another

related source discusses the tracking of an UGV by a

UAV considering changes in UGV speed and heading

but constant UAV speed (Lee et al., 2003).

Considering the forerunner concept here the UAV

needs to ﬂy in front of the EGV to have images about

forthcoming obstacles or threatening vehicles in ad-

vance. As nowadays the emergency vehicle drivers

navigate by their own GPS and probably they sud-

denly modify the route (especially in intersections)

based on their knowledge about the area its advisable

to plan and follow the route on-board the EGV and

follow the EGV with the UAV. So from the above lit-

eratue review the following of the GV by the UAV

(Lee et al., 2003) and the support of the GV opera-

tor (who is on-board the EGV in this case) through

streamed video (Harik et al., 2015) are the closest to

our concept but variable UAV speeds should be con-

sidered and the UAV must ﬂy in front of the EGV.

This paper is an introduction of the basic concepts

considered in the design and development of the Fore-

runner UAV solution. Section 2 summarizes the hard-

ware system and the ideas for the cooperative control

concept between the EGV and UAV. Section 3 sum-

marizes the computer simulation concept of the over-

all forerunner system including the EGV, the UAV, the

camera sensor model and the related virtual 3D en-

vironment. Section 4 deals with the communication

issues regarding position and route data communica-

tion between the EGV and UAV and the streaming of

video from the UAV to the EGV. Section 5 gives an

overview about the planned simulation and real life

test scenarios to evaluate the pros and cons of the fore-

runner concept and demonstrate its basic capabilities

and ﬁnally Section 6 concludes the paper.

2 SYSTEM CONCEPT

In our approach, the UAV supplements the EGV to

extend its sensing capabilities. The aerial vehicle has

to be autonomous and has to follow or get ahead of the

EGV as the circumstances require. For example if the

EGV has to go through a crowded city center, it is ad-

vised for the drone to stay ahead relatively close to the

EGV in order to inspect the upcoming intersections.

On the other hand, in a highway or motorway scenario

the drone can get further ahead to identify obstacles,

providing the driver with extra time to safely reduce

speed.

Both of these cases prove that the UAV is not nec-

essarily following its companion rather leading the

group. However, the EGV is the one that dictates

the route. This problem can be solved if the UAV

knows the proposed path of the EGV. Therefore, at

the start of the journey, the destination, the planned

route and the initial course has to be sent to the drone.

Of course, this is not a ﬁnal decision the EGV can al-

ter its route due to heavy trafﬁc or other reasons, so

the UAV has to adapt to the emergency vehicle when-

ever it is necessary. To achieve this, the drone is com-

manded to stop at every intersection and wait for the

emergency vehicle, meanwhile scanning the environ-

ment for possible hazards. After it is evident which

way the EGV is going (from communication or the

motion of the EGV) the drone starts to go to the same

direction.

To be able to observe the surroundings of the

emergency vehicle, the UAV has to be equipped with

a camera preferably attached with a gimbal to en-

able independent movement relative to the drone. The

whole concept relies on the perception of the cam-

era, therefore it has to be carefully selected. The im-

age processing is highly dependent on the point of

view of the camera. Its task is to identify pedestrians

and vehicles and their position on the ground. The

current state of development suggests, that bounding

box (YOLO (Rekha B. S., 2020)) based identiﬁcation

could be carried out real-time on the computer aboard

the drone.

From the above concept it is obvious that there has

to be some kind of communication between the UAV

and the EGV. Not only the planned route and course

has to be sent at the beginning but during move-

ment several data (including velocity and change of

plans) must be transmitted from the ground to the

aerial vehicle serving as the base of the cooperative

control. On the other hand, the UAV sends the risk

alerts, raw and/or (pre-)processed camera image to

the ground to help the driver. The place where im-

age processing is carried out is going to be deter-

mined by the performance of the on-board computer

The Forerunner UAV Concept for the Increased Safety of First Responders

363

of the drone. On-board image processing would pose

smaller challenge to the inter-vehicle communication

because post-processed images are much less data.

The communication issues are discussed in detail in

Section 4.

After the overall concept of the Forerunner UAV

system is described our proposed hardware scheme is

shown in Fig. 1.

As it can be seen in Fig. 1 our drone choice is the

DJI M600 Pro. The reason behind it is that it offers

redundancy on multiple levels, for example it features

six batteries, three sets of IMU sensors and six ro-

tors. This makes the device safer to ﬂy, which is a

key feature when using it in possibly life-threatening

scenarios and also key for the approval of airspace

authorities. The drone is equipped with the DJI A3

autopilot system which will be completed with an on-

board computer the DJI Manifold 2 which is responsi-

ble for the high-level control and data collection from

the RTK GPS unit (SCL RTK in the ﬁgure) and cam-

era module. Furthermore, the communication with

the ground vehicle is also carried out by the Manifold

2 computer.

Figure 1: Hardware concept of the Forerunner UAV system.

Considering the drone choice a serious game stop-

per in the Forerunner UAV concept can be the limited

speed range of the available drones that is why an ex-

tensive search was done to have an overview about

the possibilities. The results are summarized in Table

1 (VTOL means vertical take-off and landing). As

the table shows most of the drones are only capable

to reach about 90 km/h maximum speed which can be

satisfactory in urban environment but is not enough on

country roads and freeways. However, the Schiebel

Camcopter’s 220 km/h maximum speed makes the

concept feasible in the future even on freeways. We

also assume that drone industry continues to produce

Figure 2: Nissan Leaf experimental ground vehicle.

better and so faster drones in near future.

Using a proper emergency response vehicle dur-

ing development and testing would be cumbersome so

we plan to employ our Nissan Leaf (see Fig. 2) test

vehicle as a substitute. The regular passenger car has

now been heavily modiﬁed to become an autonomous

vehicle test platform (Somogyi et al., 2018). It is

currently mounted with several sensors such as LI-

DARs, a radar, cameras, and high precision naviga-

tion sensors. It is also equipped with several compu-

tation units to handle data and control tasks. Though

it is capable of autonomously navigating we plan to

use it in human-driven mode as the ground vehicle of

the group while also taking advantage of the already

operational navigation system, on-board processing,

and user interface devices. However, as Fig. 1 shows

we also plan to develop a standalone ground segment

with battery, board computer, RTK GPS and commu-

nication module to be able to use it on any human

driven vehicle replacing the Leaf during the develop-

ments.

3 SIMULATION CONCEPT

As the real scenario testing capabilities are very lim-

ited due to the availability of the proving ground Za-

lazone (zalazone.hu/en) and they require much more

effort we only plan to show system capabilities in low

speed urban maneuvers (see Section 5). That’s why

an extensive simulation test campaign is very impor-

tant to evaluate the possible capabilities of the Fore-

runner UAV concept in all situations (see Section 5).

A realistic forerunner drone simulation (see Fig.

3) requires a drivable EGV, a drone with simulated

camera sensor, and an environment with other vehi-

cles and pedestrians. CARLA simulator (Dosovit-

skiy et al., 2017) with Unreal4 game development

engine provides a functional city with programmed

intersections, vehicle and walker AI (artiﬁcial intelli-

gence) agents, detailed simulated sensors (RGB cam-

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

364

Table 1: Drone types with maximum cruise speed.

Structure Type Max. speed [km/h]

VTOL Quantum Systems Vector 54-90

VTOL Quantum Systems Tron F90+ 64.8

VTOL UKR Spec Systems PD-1 90

VTOL L3Harris FVR-90 92.6

VTOL Blackswift S2 64.8

Quadcopter DJI M300 82

Quadcopter DJI Inspire 2 94

Quadcopter Freeﬂy Alta X 95

Hexacopter DJI M600 Pro 64

Hexacopter Yuneec Typhoon H 70

Helicopter Schiebel Camcopter S-100 220

Figure 3: Fire truck with forerunner drone in intersection.

era, depth camera, LIDAR etc.) and a python API.

Into this solid basis we integrate our forerunner UAV

with the on-board camera system, our test scenarios

with other vehicles, and our image processing and de-

cision making logic. We plan to drive the EGV with

game controllers to make demonstrations more inter-

esting by involving the bystanders to drive the vehicle.

The dynamics of the Forerunner UAV is simulated in

Matlab (visulaization done in CARLA) and an UDP-

based communication (similar to the real system) is

implemented between the Matlab (UAV) and CARLA

(EGV) part (see Fig. 4).

Figure 4: Main components of the simulator and their tasks.

Fig. 4 summarizes the main components of our

simulator. The basis of the simulator is CARLA

which can be modiﬁed in Unreal editor. A ﬁretruck

is added as a version of an existing drivable truck

in CARLA, while the drone is a base vehicle pawn,

which can be manipulated through the python API

according to our own Matlab simulation. PyGame

part is responsible for the user interface of the driver

(steering wheel or keyboard). PyVision is our image

processing and decision making module, which can

run on a separate computer.

The Matlab Simulink UAV simulation incorpo-

rates detailed attitude and position dynamics, mod-

eling of the propulsion system and the implemented

control method. The control loop uses the incom-

ing data from the EGV (velocity and planned route)

to be able to maintain a certain distance in front of

it. After calculating the displacement and heading of

the drone, the data is sent to the CARLA simulation

which then draws the model accordingly. The param-

eterization of the drone model is done to represent a

similar model to the DJI M600 Pro planned to be ap-

plied during the real life tests of the Forerunner UAV

system.

The drone should detect other vehicles, cyclists

and pedestrians to decide if the EGV can pass the

intersection at high speed or not. To do this, a 3D

semantic map of the neighborhood of the intersec-

tion should be generated from camera images (or

from higher altitudes a 2D semantic map with YOLO

for example). The simulation environment will also

give us the possibility to develop gimbal management

strategy(s) for the monitoring of the intersection, and

to deﬁne the required time and frame rate for decision

on the safe EGV pass.

The ﬁrst version of our image processing is based

on a semantic segmentation of drone camera accord-

ing CityScapes concept (Cordts et al., 2016). The out-

put of the simulator is not given back to the PyGame

module because we will possibly send only decisions

to the EGV from the UAV if the wireless communi-

cation capabilities are too limited. However, we plan

The Forerunner UAV Concept for the Increased Safety of First Responders

365

to stream the video to the EGV and it is possible that

processed images will also be sent to the EGV to give

more insight in the case of a stalled situation.

After simulation testing in urban scenarios coun-

try road and freeway scenarios will be implemented

and tested as listed in Section 5.

4 COMMUNICATION ISSUES

As stated before, the communication between the

UAV and the EGV will be crucial in the success of the

Forerunner UAV system that is why extensive com-

parison of possible solutions and even communica-

tion system tests are conducted in this early stage of

the project.

One of the best possible solutions is to establish

a connection according to the IEEE 802.11 standard.

Nowadays these systems work on the 2400-2483.5

MHz, 5150-5875 MHz, and 57-71 GHz frequency

bands. The devices that use 802.11 protocol for com-

munication are simple radio units, but these systems

are robust and interference tolerant.

The standard uses the ISM frequency bands for

communication. The ISM bands are free to use, so

the operation on these frequencies is only limited by

the regional legislation. Furthermore, the 802.11 stan-

dard’s additional advantage is the 15-20 ms typical

response time (this is the minimal expected limit).

The popularity of the standard is shown by the

fact that throughout the years several versions of the

original standard has been published. The new stan-

dards introduced new frequency band usage, higher

data rate, and more robust modulations. The current

newest standard is the Wi-Fi 6, which is described in

the 802.11ax standard. Table 2 shows the data rate

calculation for 802.11ac standard.

Values shown in the table are not the maximum

data rates that the standard describes (256-QAM).

The selection was based on several factors, like the

phase distortion caused by motion, and destructive

or additive interference according to the phase differ-

ence of multipath signals. As for the code ratio, the

same pattern can be seen. The chosen values are more

robust than the highest ratio of the standard. Regard-

ing the guard interval (GI) the table has calculations

for 400 ns and 800 ns as well. Considering the ap-

plication, the higher 800 ns values can guarantee the

fail-safe functionality in an environment with reﬂec-

tions. The calculations for the video signal should be

evaluated by these factors.

The Forerunner UAV application can apply two pos-

sible variations:

• FHD video signal streaming to a vehicle, where

the post-processing happens;

• The post-processing happens on-board of the

UAV, and the results are being transferred to the

vehicle in an array.

In the ﬁrst situation, calculating with the display

ratio (1920x1080) and using the H.264 (HVC) video

compression, 8.5 Mb/s data rate can be estimated.

While with the H.265 (HEVC) compression only 4.4

Mb/s for a 30 frame/sec video stream.

In the second version, the display format should

be at least 4CIF which is 704x480 pixel, with the

application of H.264 compression there should be

around 1.6 Mb/s regarding the data rate. Meanwhile

the H.265 method should provide the average 0.8

Mb/s data rate.

For the calculations, the urban areas are the most

critical (see Fig. 5). In these areas where the device

distributions are very dense, the proper performance

must be guaranteed. The interference in these areas

is signiﬁcantly higher and the systems must change

to less crowded frequency band (for stable data trans-

mission) even in a 100 m radius. In case of Active

Scan, it needs higher data rate (min. 4 Mb/s) for a

30 ms scanning every 5 second. If the vehicle moves

with 50 km/h there will be around 70 m distance be-

tween two measurement.

Figure 5: Number of Access Points in Budapest (bobzilla

et al., 2020).

Another problem regarding the 802.11 standard,

is that the devices can order more users to the avail-

able spectrum. The regulations deﬁne three layers of

users. The second- and third-layer devices cannot in-

terfere with the ﬁrst layer applications. This applies to

the second- and third-layer’s relationship too. In con-

clusion the third layer devices cannot cause any in-

terference to the ﬁrst- and second-layer systems. The

previously mentioned applications in the 2400-2483.5

MHz and 5150-5875 MHz frequency bands are third

layer devices.

The other possibility is the civilian MFCN net-

work with LTE and NR services. These technologies

have similar or lower response time than the 802.11

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

366

Table 2: Maximum theoretical data rates.

Spatial stream Modulation Code ratio

Maximum theoretical data rate

20 MHz channel 40 MHz channel

GI 800 ns GI 400 ns GI 800 ns GI 400 ns

1x1 SISO 16-QAM 1/2 26 Mb/s 28.9 Mb/s 54 Mb/s 60 Mb/s

1x1 SISO 16-QAM 3/4 39 Mb/s 43.3 Mb/s 81 Mb/s 90 Mb/s

1x1 SISO 64-QAM 2/3 52 Mb/s 57.8 Mb/s 108 Mb/s 120 Mb/s

1x1 SISO 64-QAM 3/4 58.5 Mb/s 65 Mb/s 121.5 Mb/s 135 Mb/s

2x2 MIMO 16-QAM 1/2 52 Mb/s 57.8 Mb/s 108 Mb/s 120 Mb/s

2x2 MIMO 16-QAM 3/4 78 Mb/s 86.7 Mb/s 162 Mb/s 180 Mb/s

2x2 MIMO 64-QAM 2/3 104 Mb/s 115.6 Mb/s 216 Mb/s 240 Mb/s

2x2 MIMO 64-QAM 3/4 117 Mb/s 130.3 Mb/s 243 Mb/s 270 Mb/s

systems. For the LTE connections the average re-

sponse time is around 10-15 ms. The 5G NR system’s

can be 1ms (NOKIA, 2016) depending on the applica-

tion. These particular systems work with wider range

of frequency bands (700 MHz, 800 MHz, 1800 MHz,

2100 MHz, 3,5 GHz, . . . ) where they have priority

channel utilization. The above-mentioned bands are

dedicated for a speciﬁc service provider. In this sit-

uation the placements of base stations are limited to

more occupied areas, like cities and roads. In rural

areas only the UMTS network can be ensured, which

cannot provide the appropriate values for the commu-

nication.

Overall, the urban areas are suitable for MFCN (or

not civilian PPDR and BBDR) system usage, and ru-

ral areas are convenient for the 802.11 networks.

A preliminary test was also run to determine the

latency of wireless image transmission between two

hosts. IEEE 802.11n compliant devices were used op-

erating in the 5 GHz mode with the use of two spatial

streams at a channel width of 40 MHz. The sender

was connected to a Cisco E2000 wireless router via

WLAN, while the receiver communicated with the

router on Gigabit ethernet. Using the 64-QAM modu-

lation scheme the standard provides a 300 Mb/s max-

imum theoretical data rate. In our case an effective

data rate of 115 Mbit/s was measured. We applied

NewTek’s NDI API to produce video streams both in

low and high resolutions. In both cases the frame rate

was 20 fps. Table 3 shows the results.

Table 3: Latency of Video Transmission Tests.

Resolution Latency

640x480 30 ms

1440x1080 40 ms

Table 4: List of Abbreviations.

Abbreviation Deﬁnition

4CIF

4xCommon Intermediate For-

mat

AP Acces Point

BBDR

Broadband Public Protection

and Disaster Relief

FHD Full High Deﬁniton

GI Guard Intervall

LTE Long Term Evolution

MFCN

Mobile/Fixed Communication

Network

MIMO Multiple Input Multiple Output

NR New Radio

PPDR

Public Protecion and Disaster

Relif

QAM

Quadrature Ampltide Modula-

tion

SISO Single Input Single Output

WLAN Wireless Local Area Network

5 TARGETED TEST SCENARIOS

The goal of the simulation campaign is to extensively

test the capabilities of a possible forerunner system

considering scenarios from urban to freeway as ﬁrst

responders can operate in any of these scenarios. The

planned test scenarios and situations are:

• EGV approaches an intersection with trafﬁc light

(red to the EGV) or priority to the right and

crosses straight. Car coming from right stops or

not.

• EGV approaches an intersection with trafﬁc light

(red to the EGV) or priority to the right and turns

left. Car coming from right and/or front stops or

not.

• EGV approaches a crowded intersection with traf-

ﬁc light (red to the EGV) or priority to the right

The Forerunner UAV Concept for the Increased Safety of First Responders

367

and crosses straight. Cars before the crossroad get

out of the way except for one and/or car coming

from right stops or not.

• EGV approaches a roundabout where other vehi-

cles stop to give way or not.

• EGV drives with high speed on a country road,

other vehicles give way on the road and at cross-

roads or not.

• EGV drives with high speed on a curved country

road, other vehicles give way or not.

• EGV approaches a slow vehicle on a country road

is it possible to overtake or not?

• EGV drives on a freeway, other vehicles give way

or not, vehicle with technical breakdown causes a

problem.

The goal of the real demonstration is to show

the basic capabilities of a Forerunner UAV sys-

tem. Considering the features of the targeted proving

ground Zalazone (zalazone.hu/en) this demonstration

is planned in a low speed artiﬁcial urban environment,

the so called Smart City. The planned scenarios are:

• EGV approaches an empty intersection with pri-

ority to the right and crosses it straight.

• EGV approaches an intersection with priority to

the right and crosses it straight. Car coming from

right stops, EGV continues.

• EGV approaches an intersection with priority to

the right and crosses it straight. Car coming from

right does not stop, EGV must brake.

• EGV approaches an empty intersection with pri-

ority to the right and turns left.

• EGV approaches an intersection with priority to

the right and turns left. Other vehicles coming

from front and/or right all stop, EGV continues.

• EGV approaches an intersection with priority to

the right and turns left. Other vehicles coming

from front and/or right do not stop all, EGV must

brake.

6 CONCLUSIONS

This paper introduces the Forerunner UAV concept

which to the best of the authors knowledge is a new

idea. It includes an UAV equipped with an on-board

camera ﬂying in front of the emergency ground vehi-

cle (EGV) and checking if its right of way is provided

or not. It can notify the driver, stream the video to the

driver of the EGV and in improved versions can also

highlight the dangerous vehicles / spots on the video

image. This can decrease the risk of trafﬁc accidents

involving EGVs and so increase the safety of ﬁrst re-

sponders.

After presenting this concept the article introduces

the main topics discussed during the early develop-

ment of the system and the ﬁndings of these discus-

sions.

The overall system concept is based-on the coop-

erative control of the EGV and UAV where the UAV

should ﬂy in front of the EGV knowing its planned

route but stopping and waiting for it in every intersec-

tion to be able to adapt to sudden route changes. The

DJI M600 Pro hexacopter was selected as the UAV

because of its triple redundant architecture and pay-

load capacity. The demonstration ground vehicle is

planned to be the Nissen Leaf vehicle of the research

group but an independent ground segment will be de-

signed which can be easily installed on any ground

vehicle with proper load capacity.

As the planned real demonstrations will cover

only a limited subset of the required capabilities of

the system an extensive simulation test campaign is

planned to evaluate all of the capabilities. This is built

on the CARLA simulator with Unreal engine simulat-

ing the EGV and its environment. The UAV is simu-

lated in Matlab Simulink and an UDP protocol-based

communication similar to the real one provides the

connection between them. Simulated camera images

are generated in CARLA and the image processing is

planned to be done on a separate computer.

As the communication of planned route and the

video stream between the EGV and UAV is a crucial

safety critical part of the system a separate section is

devoted to the communication possibilities and sys-

tem tests focusing on data rates and delays.

Finally, the last section summarizes the planned

simulation and real demonstration scenarios attempt-

ing to cover the whole range of possible situations.

Future work includes the selection of the commu-

nication devices and protocols, the detailed design,

tuning and testing of the cooperative EGV-UAV con-

trol, the detailed set up of the simulation environment

together with the generation of the 3D semantic map

and the decision about the safety of the EGV and the

evaluation of the results. The real demonstration sce-

narios will be reﬁned and set up based on the simula-

tion results and experiences.

REFERENCES

bobzilla, arkasha, and uhtu (2001-2020). Wigle.net All the

networks. Found by everyone.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

368

Cantelli, L., Mangiameli, M., Melita, C. D., and Muscato,

G. (2013). UAV/UGV cooperation for surveying oper-

ations in humanitarian demining. In 2013 IEEE Inter-

national Symposium on Safety, Security, and Rescue

Robotics (SSRR), pages 1–6.

Casey C. Grant, P. and Merriﬁeld, B. (2011). Analysis of

Ambulance Crash Data. Technical report, Fire Protec-

tion Research Foundation.

CNN (2019). Car hit by train after avoiding crossing arm.

Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler,

M., Benenson, R., Franke, U., Roth, S., and Schiele,

B. (2016). The cityscapes dataset for semantic urban

scene understanding. In Proc. of the IEEE Conference

on Computer Vision and Pattern Recognition (CVPR).

Donoughe, K., Whitestone, J., and Gabler, H. C. (2012).

Analysis of ﬁretruck crashes and associated ﬁreﬁghter

injuries in the united states. Annals of advances in

automotive medicine. Association for the Advance-

ment of Automotive Medicine. Annual Scientiﬁc Con-

ference, 56:69–76.

Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and

Koltun, V. (2017). CARLA: An open urban driving

simulator. In Proceedings of the 1st Annual Confer-

ence on Robot Learning, pages 1–16.

fandom.com (2020). Halo alpha.

Forerunner Computer Systems (2020). Forerunner Com-

puter Systems - Making Computers Simple.

Garmin (2020). Garmin forerunner 745 watch.

Harik, E. H. C., Gu

erin, F., Guinand, F., Breth

e, J., and

Pelvillain, H. (2015). UAV-UGV cooperation for ob-

jects transportation in an industrial area. In 2015

IEEE International Conference on Industrial Technol-

ogy (ICIT), pages 547–552.

Inside Unmanned Systems (2019). One Ultra-Endurance

LEAP for UAVs.

Lakas, A., Belkhouche, B., Benkraouda, O., Shuaib, A., and

Alasmawi, H. J. (2018). A Framework for a Cooper-

ative UAV-UGV System for Path Discovery and Plan-

ning. In 2018 International Conference on Innova-

tions in Information Technology (IIT), pages 42–46.

Lee, J., Huang, R., Vaughn, A. F., Xiao, X., Hedrick, J.,

Zennaro, M., and Sengupta, R. (2003). Strategies of

Path-Planning for a UAV to Track a Ground Vehicle.

In In Proc. of the AINS 2003 conference.

NHTSA (2014). The National Highway Trafﬁc Safety Ad-

ministration and Ground Ambulance Crashes.

NOKIA (2016). 5G for Mission Critical Communication.

Technical report, Nokia.

Peterson, J., Chaudhry, H., Abdelatty, K., Bird, J., and

Kochersberger, K. (2018). Online Aerial Terrain Map-

ping for Ground Robot Navigation. Sensors, 18(2).

Rekha B. S., Athiya Marium, D. G. N. S. S. A. S.

(2020). Literature survey on object detection using

yolo. International Research Journal of Engineering

and Technology (IRJET).

Smith, N. (2015). A National Perspective on Ambu-

lance Crashes and Safety. Guidance from the National

Highway Trafﬁc Safety Administration on ambulance

safety for patients and providers. EMS world, 44:91–

2, 94.

Somogyi, H., Pup, D., K

os, P., Mihaly, A., and Soume-

lidis, A. (2018). Research of Required Vehicle System

Parameters and Sensor Systems for Autonomous Ve-

hicle Control. In 2018 IEEE 12th International Sym-

posium on Applied Computational Intelligence and

Informatics (SACI), pages 000027–000032.

Zoto, J., Musci, M. A., Khaliq, A., Chiaberge, M., and

Aicardi, I. (2020). Automatic Path Planning for Un-

manned Ground Vehicle Using UAV Imagery. In

Berns, K. and G

orges, D., editors, Advances in Ser-

vice and Industrial Robotics, pages 223–230, Cham.

Springer International Publishing.

The Forerunner UAV Concept for the Increased Safety of First Responders

369