Unity 3D Simulator of Autonomous Motorway Traffic Applied to

Emergency Corridor Building

Jurij Kuzmic

and Günter Rudolph

Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, Germany

Keywords: Simulator in Unity 3D, Emergency Corridor Simulation on Motorways, Accident Simulation, Autonomous

Driving, Vehicle-to-Vehicle (V2V) Communication, Lane Detection, Distance Detection, Rotation Detection.

Abstract: This paper introduces a 3D simulator made with the game engine Unity to analyse the behaviour of

autonomous vehicles in the case of unexpected accidents in motorway traffic. This simulator works towards

the removal of current problems with building an emergency corridor on motorways. It is often the case that

rescue vehicles cannot reach the scene of an accident and are obstructed by other road users. This means that

the help for those involved in the accident may come too late. To prevent this in future with autonomous

vehicles and to save human lives, building an emergency corridor for self-driving cars will be simulated and

presented with the game engine Unity. Since the autonomous vehicles also have to communicate while driving,

the techniques of Vehicle-to-Infrastructure (V2I) Communication, Vehicle-to-Vehicle (V2V) Communication

and Infrastructure-to-Infrastructure (I2I) Communication will be reviewed theoretically. Besides, practical

methods for lane, distance and rotation detection will be presented. Furthermore, we discuss sensor

technology such as position estimator, lidar, radar and video camera. Also, the levels of automation of self-

driving cars will be shown. This will make it possible to determine the level of the automated rescue corridor

formation. Several experiments prove the simulator’s functionality concerning unexpected accidents and the

formation of the rescue corridor. Finally, further research and work in this area will be explained briefly.

1 INTRODUCTION

The autonomous vehicles industry is trying to

improve the safety and therefore to reduce the

accidents caused by autonomous vehicles as much as

possible. Nevertheless, accidents could be due to

technical defects or unexpected software errors. In

order to map the behaviour of unexpected accidents

with autonomous vehicles, a simulator has been

implemented with Unity 3D. Before starting the

implementation of the simulator, an established

traffic flow simulator has been searched. Some

simulators are available: PTV Vissim (PTVGroup,

2019), MATSim (MATSim, 2019), SUMO

(Behrisch, Bieker, Erdmann and Krajzewicz, 2011),

MAINSIM (Dallmeyer and Timm, 2012).

Unfortunately, the behaviour of the car cannot be

freely programmed. In addition, the position of the

car cannot be freely changed. This is the reason why

an own simulator has been implemented with Unity.

This allows to affect the behaviours of the car, the

communication between vehicles and the detection of

the lane, distance, rotation and accident in relation to

a traffic flow simulator. The motorway can also be

freely designed. For instance, several lanes can be

added, which can vary in width.

There are already several projects in Unity that

deal with the simulation of autonomous vehicles. For

example, roads with cars, pedestrians and

autonomous vehicles with sensors can be created (De

Oliveira, 2018). Some projects even include self-

driving cars with image recognition of the simulated

environment (De Oliveira and Duong, 2018). We

wanted to simulate the building of the rescue corridor

with autonomous vehicles on motorways, among

other things. So we needed the ability to create

motorways with crash barriers and multiple lanes. For

this reason, we implemented our own simulator. To

the best of our knowledge, there is still no published

research in the field of building an emergency

corridor with autonomous vehicles automatically.

Kuzmic, J. and Rudolph, G.

Unity 3D Simulator of Autonomous Motorway Trafﬁc Applied to Emergency Corridor Building.

DOI: 10.5220/0009349601970204

In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 197-204

ISBN: 978-989-758-426-8

197

2 AUTONOMOUS VEHICLES IN

PRACTICE

In order, to implement a suitable simulator for

unexpected accident simulations with autonomous

vehicles on motorways, the technology of

autonomous vehicles was examined focusing on

practical applications.

2.1 Lane, Distance and Rotation

Detection

This section discusses briefly the lane detection, the

distance detection, the rotation detection and the used

sensors in practice. Usually, the detection of lane or

keeping the vehicle on track is carried out by image

recognition via the video camera. Usually, this is

installed in the front windshield. The perception of

the urban environment is done by a rotatable sensor

called lidar on the roof of the vehicle. Thereby 3D

mapping (3D environment) can be created. The

distance sensors (radar) are necessary for automatic

parking and getting the distance to the front vehicle.

The position estimator is required for the vehicle’s

rotation detection and the determination of the

position of the vehicle (Azmat and Schuhmayer,

2015).

2.2 Communication between

Self-driving Cars

In practice, communication between autonomous

vehicles is often divided into three communication

options. The first is Vehicle-to-Infrastructure (V2I)

Communication. For example, this allows to transmit

the current colour of the traffic light to the arriving

vehicle. The second option is Vehicle-to-Vehicle

(V2V) Communication. In this way, an accident

vehicle can notify the other vehicles in the urban area.

The third is Infrastructure-to-Infrastructure (I2I)

Communication. I2I Communication can be used to

switch traffic light systems intelligently to avoid

traffic jams. To enable communication, a vehicular

ad-hoc network (VANet) is set up (Hartenstein and

Laberteaux, 2008). This is a mobile ad-hoc network

(MANet), whose nodes for message distribution are

the vehicles themselves. These vehicles have On-

Board Units (OBUs) that are responsible for

communication between the vehicles and road-side

units (RSUs) (Alheeti, Gruebler, McDonald-Maier,

2016).

2.3 Levels of Automation

This section surveys the levels of automation for the

autonomous vehicle. These are divided into five

levels. The classification of the level depends on the

technologies used in the vehicle and the driver’s

intervention in the driving process (Urooj, Feroz and

Ahmad, 2018). The following table 1 describes these

levels.

Table 1: Levels of automation (NHTSA) (Leftlaneadvisors,

2013).

Level Automation

Level 0

No vehicle autonomy,

Driver has Control

Level 1

Vehicle provides driver info/warnings,

Driver has informed control

Level 2

Vehicle integrates detection/response,

Driver ready to take control

Level 3

Vehicle fully autonomous,

Driver takes control in emergency

Level 4a

Vehicle fully autonomous,

Occupants do not need ability to drive

Level 4b

Vehicle connected and cooperating,

Optimised System operation and passive

driver experience

The self-driving car could build an emergency

corridor automatically after the third level of

automation. From this level, the vehicle is completely

autonomous. The driver only takes part in the driving

process in dangerous situations.

3 SIMULATION IN UNITY 3D

Based on how the autonomous vehicles work in

practice, the requirements for a suitable

implementation of the simulator could be derived. To

ease the implementation effort, the asset “Car'Toon :

The Sport Car with interior” was retrieved from the

Unity Asset Store (Unity Asset Store, 2019). It

already contains the implementation of the steering

behaviour, acceleration, braking and physics of a car.

So the car could be successfully used in our project.

During the implementation of the automated

emergency corridor formation, it was noticeable that

the car could not be steered during emergency

braking because the wheels of the car from this

example completely block during braking with all

four wheels. In order to be able to build an emergency

corridor, it is not only necessary to brake during

emergency braking, but also to steer. In practice, this

is only possible with Anti-lock Braking System

(ABS).

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As a result, this was also implemented in this

simulator by hand. In a dangerous situation, a human

cannot brake and think about building an emergency

corridor at the same time. For comparison, the

autonomous vehicle is able to do this, because it does

not react nervously and hectically towards the human.

3.1 Lane, Distance and Rotation

Detection

This section deals with the detection of the lane, the

detection of the distance to the following vehicle and

the detection of the rotation of the car in the presented

simulator. For simplicity, the detection of the lane

was implemented in a different way than known from

practice (image recognition with video camera). Ray

casts from Unity were used to determine the width of

the lane to keep the vehicle on track. This replaces the

image recognition with a video camera. Ray casts can

be imagined as a line in a certain direction with a

certain length.

As soon as a game object is hit by this line, it also

provides the name and distance to this object in a

three-dimensional space. This allows to determine the

object which has been hit and how far it is from the

ray origin (Fig. 1).

Figure 1: No hit/hit ray cast example.

For the detection of the lane, two ray casts were used

(Fig. 2, left-top). Both are directed from the vehicle

to the front and they slightly look downwards (Fig. 2,

left-bottom). Ray casts can be used to simulate not

only lane detection, but also distance sensors (radar).

In practice, radar determines the distance to the front

vehicle. The various practical sensors are described

more detailed in section 2. The nine forward ray casts,

shown in figure 2, are necessary to determine the

distance to the following vehicle. As soon as the

distance can be measured, a function calculates the

safety distance to the following vehicle on the base of

the driving speed.

The implemented rotation detection of the own

vehicle includes the monitoring of the rotation per

frame. This implementation simulates the sensor for

the position estimation from practice. As soon as a

large rotation has taken place in a short time, an

unexpected rotation of the vehicle can be assumed.

This rotation detection helps to simulate the bursting

of a tire while driving. More about this experiment

can be found in section 4. In addition to the rotation,

the collision of the vehicle is also monitored. This

method is provided by Unity and is automatically

called when a game object collides with another game

object. As soon as one of these two accidents is

detected, the self-driving car makes an emergency

stop, switches on the indicator warning lights and

notifies the self-driving cars in the area. This scenario

is explained in more detail in the next subsection.

Figure 2: Ray casts of a self-driving car for lane and

distance detection. Left-top: Ray casts for lane detection.

Left-bottom: Top-view of a car. Right: Side-view of a car.

Based on own experiments, an effective function (Eq.

1) for the braking distance could be found for this

simulation. This function provides the braking

distance (bd) based on the vehicle's driving speed (s).

However, this is only the braking distance, which has

to be kept in any case. In our used function (Eq. 2), a

linear value (d) for the safety distance (sd) was added

to the braking distance (bd).

  0.00004



 0.0135



 0.0601

 0.02219

(1)

    

(2)

Other models are imaginable and will be tested in

future.

3.2 Communication between

Self-driving Cars

Communication between autonomous vehicles is

very important. This exchange of messages between

autonomous vehicles is like a warning by flashing

indicator warning lights or brake lights for humans. It

is also important that the communication takes place

from vehicle to vehicle. This means that the

communication works without a central point, such as

Unity 3D Simulator of Autonomous Motorway Trafﬁc Applied to Emergency Corridor Building

199

a pole. This can fail or be manipulated. The

coordination of the self-driving cars can also be

realized by the communication. As an example, all

vehicles stop unexpectedly. That raises the questions:

Who has to do something first? Here it is also difficult

to map the rules. How do vehicles behave in the event

of an accident, for example, due to a technical defect?

Who drives by? Who stops? Even for a human, in

some situations it is difficult to decide how to behave

correctly.

Communication can also be used to find out the

lane position of the vehicle and the number of lanes

of the motorway in total. The autonomous vehicles

can independently recognize the environment and

communicate this to the next autonomous vehicles.

Through unique identification numbers (IDs), the

vehicles know which vehicle is in front, behind, left

and right. As soon as an accident is detected, this has

also to be passed on to the following vehicles. As

soon as a message arrives, the lidar on the roof of the

vehicle turns yellow, which is still currently under

construction in this paper. This colouring makes the

arrival of the messages visible for the human eye.

Communication between agents can be implemented

with Unity in two different ways. Both approaches

have been tested for communication between

vehicles. The first approach utilises a sphere given a

centre point and a radius. All objects inside this

sphere can be located. This allows to quickly

determine which vehicles are nearby. At this point,

the own vehicle is also delivered. This has of course

to be ignored in this case. Figure 3, left shows the

sphere in use. The centre of this sphere was placed at

the centre of the autonomous vehicle.

Figure 3: Communication between agents. Left:

Communication with sphere. Right: Communication with

ellipse.

In the previous figure 3, left can also be seen, that the

sphere recognises the objects above and below the

vehicle. Sending the messages above or below is not

necessary in this case. This does not correspond to

reality. In practice, the signal would not be wasted

upwards or downwards. This would be a signal in a

certain direction by directional antennas. This allows

reaching larger distances with the same transmission

power. For this reason, a separate ellipse was

implemented. The figure 3, right shows this ellipse

graphically. In this case, the communication ellipse is

a game object that is located in the vehicle as a sub

game object. This contains methods to trigger the

objects and a dictionary with the collided game

objects. These collisions are called haptic and not

physical collisions. The methods are called

automatically if a collision occurs or leaves. This

happens in the background, so the dictionary with the

collided vehicles is always up to date.

As soon as an accident is detected, the vehicle

performs an emergency stop, switches on the

indicator warning lights and inform the vehicles in the

surrounding area. The vehicles that receive the

message, decide on their own how to react to this

message. The direction and the position of the

vehicles are compared. Only the vehicles that are

moving in the same direction and are behind the

accident vehicle slow down and forward this message

to the rear vehicles. The other vehicles that are nearby

and do not have to brake, as there is no danger for

these vehicles, accept the message and inform the

emergency services. The experiments show that the

detection of the vehicles in the surrounding area with

the ellipse as a sub game object works faster. Thus,

the messages can be transmitted faster to the vehicles

in the surrounding area. The reason for this, the haptic

collision detection of collided vehicles in the

surrounding area is located in the background and is

always up to date at any time of the program. With

the sphere, on the other hand, the vehicles in the

surrounding area are calculated each time the

messages are sent. With many messages per vehicle,

this of course takes more time. A message controller

class has been implemented to control the incoming

and outgoing messages. This is necessary for the

storage of the messages. A message contains the

following information: transmitter id, outgoing

message, receiver id, incoming message, message

enter time, accident car drive direction and accident

car position. Currently can be responded to the

outgoing message like “SPIN”, “ACCIDENT” and

“FORWARD”. The stored messages are always kept

up to date. Each time a message is received, the

current incoming data is updated. The vehicles, that

decide the message is not relevant, for themselves

save the message and contact rescue.

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4 EXPERIMENTS

The following experiments were carried out to control

the functionality of the simulator. In the event of an

accident, the formation of the rescue corridor is very

important. Unforeseeable accidents happen again and

again on roads and motorways. Serious accidents can

sometimes occur on motorways because vehicles

move at high speed. This leads to traffic jams and

road closures. In this case, there is a regulation for the

formation of an emergency corridor for the

emergency vehicles arriving at the scene of the

accident. However, unfortunately this is just the

theory. The drivers of motor vehicles sometimes

forget to form a rescue corridor in a traffic jam. In

some cases, it is no longer possible to build an

emergency corridor at a later point of time. When the

vehicles come to stand, they are usually too close to

each other. In addition, due to the daily commute to

work on the motorway, it is also noticeable that even

if a rescue corridor is formed and an emergency

vehicle drives past, the drivers of the motor vehicles

close this emergency corridor again. This always

leads to obstruction of the rescue vehicle. The

problem is already known and has been discussed for

some time (Dębiński, Jukowski and Bohatkiewicz,

2018). This does not only occur on motorways, but

also in cities. There is currently no optimal solution

for this problem. Some countries impose heavy fines

for the obstruction of emergency vehicles. To

counteract this problem, a warning system for an

emergency vehicle (Emergency Vehicle Warning

System) has already been introduced (Buchenscheit,

Schaub, Kargl and Weber, 2010). To solve this

problem, the emergency corridor has to be formed

automatically by the self-driving cars that will soon

be on the roads. The question is not whether, but when

the autonomous vehicles will come onto our roads.

For building an emergency corridor with

autonomous vehicles in the presented simulator, it is

necessary to know on which lane the vehicle is

currently located and how many lanes the motorway

has in total. For example, on a motorway with two

lanes or more, the vehicles on the left lane drive even

more left to the crash barrier. On the other hand, the

vehicles on the right lane drive more to the right onto

the lane marking. The ray casts are accessed again to

compare the position of the left and right front wheel

with the lane markings. This can be used to determine

in which direction (left or right) the car has to move

to build an emergency corridor. As soon as the speed

falls below a certain value, the vehicles automatically

form a rescue corridor. This means that an emergency

corridor is always build in the event of slow-moving

traffic or traffic jams. This can be seen in the figures

in section 4.1.

The algorithm (Fig. 4) for the formation of the

rescue corridor works in accordance with a simple

principle. The numbering of the lanes starts at the

middle crash barrier.

Figure 4: Algorithm of building an emergency corridor.

4.1 Building an Emergency Corridor:

Slow-moving Traffic

The emergency corridor is always formed as soon as

the speed falls below a certain value (30 km/h). In the

case of slow-moving traffic, the vehicles

automatically form a rescue corridor. This can be seen

in figure 5, left. Figure 5 also shows that all vehicles

drive in one row in order. This is done by keeping the

safety distance to the front vehicle and drive at equal

speed. Therefore, the road is used completely as well.

This also ensures that the vehicles stop in time to

avoid injuring the passengers. As soon as the distance

to the following vehicle increases, the vehicles

accelerate automatically. When the speed increases,

the vehicles automatically increase the safety distance

and close the gap for the emergency vehicles (Fig. 5,

right).

Figure 5: Building an emergency corridor at slow-moving

traffic on a two-lane motorway. Left: Open an emergency

corridor. Right: Close an emergency corridor.

Unity 3D Simulator of Autonomous Motorway Trafﬁc Applied to Emergency Corridor Building

201

4.2 Building an Emergency Corridor:

Obstacles in Front

This experiment shows the formation of the rescue

corridor when approaching any obstacles. For

example, this could be an accident that blocks the

total road. The next figure 6 shows the formed rescue

corridor in the event of an accident on a two-lane

motorway.

Figure 6: Building an emergency corridor in the event of an

accident on a two-lane motorway. Left: Front-view. Right:

Rear-view.

The automatic building of an emergency corridor in

the case of an accident was also tested on a three-lane

motorway. This can be seen in the next figure 7.

Figure 7: Building an emergency corridor in the event of an

accident on a three-lane motorway. Left: Front-view with

normal lanes. Right: Front-view with extra wide lanes.

Figure 8: Building an emergency corridor on a three-lane

motorway with normal lanes (Rear-view).

The vehicles know the width of the lane. Thus, this

width can be incorporated into the formation of the

rescue corridor. Therefore, the width of the lane can

vary. This can be seen in figure 7, right and figure 8.

To ensure that the simulation also works for more

than three lanes of a motorway, a four-lane motorway

(Fig. 9) was also built and tested successfully.

Figure 9: Building an emergency corridor in the event of an

accident on a four-lane motorway.

4.3 Building an Emergency Corridor:

Front Tire Bursting

To investigate whether the rescue corridor can be

formed at any unexpected time, an unexpected

accident was simulated while driving. This showed

that the autonomous vehicles at any opportunity could

build an emergency corridor. The simulation was

random to cover every possible case. Therefore, the

vehicle for the accident, the rotation in degrees and

the time of the accident were determined by chance.

This simulation is intended to simulate the bursting of

a front tire while driving. The following figure 10

illustrates this example.

Figure 10: Simulation of a front tire bursting while driving.

Left: Not built emergency corridor. Right: Built emergency

corridor.

The vehicles in front of the accident vehicle continue

driving and contact the rescue service. The other

vehicles stop to avoid a collision. Figure 10, right also

shows that the car’s lidar has coloured yellow. This

illustrates the arrival of a message. Due to these

experiments, the unexpected and unimaginable

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behaviour through the physics of the vehicle can be

shown. Above all, the reaction to the adjustment of

the rotation was always different. This results from

the G-forces, which are dependent on the direction

and rotation of the vehicle. Further experiments and

considerations of this work can be found in section 6.

5 CONCLUSIONS

The presented simulator helps to demonstrate the

behaviour of autonomous vehicles in the event of

unforeseeable accidents in motorway traffic. The

vehicles for the simulation were imported from the

Unity Asset Store and extended in our project. The

lane and distance detection was performed with ray

casts. The rotation detection of the self-driving car is

done by monitoring the own car spin on the road. The

collision detection could be realised by the methods

provided by Unity. The vehicles move autonomously.

These automatically accelerate, keep the safety

distance, adjust the safety distance to their speed and

adjust the emergency corridor to the width of the lane.

If the speed falls below a certain value, an emergency

corridor will be opened automatically. This is

important in slow-moving traffic. If the speed

exceeds a certain value, the rescue corridor will be

closed automatically. Communication between

vehicles is very important in simulation and practice.

The vehicles have to exchange messages about the

situation on the road. This allows to make decisions

and to forward the direction or the position of the

accident vehicle. The exchange of messages is

realised via an ellipse, which contains the vehicles in

the surrounding area as a game object. Some

experiments were carried out to test the functionality.

These test cases were randomly generated to cover as

many different behaviours as possible. The

communication between autonomous vehicles in

practical applications was also briefly explained.

Below, lane, distance, rotation and urban

environment detection in practice have been

discussed. Furthermore, the sensors used in

autonomous vehicles were explained. The levels of

automation were also presented to be able to integrate

the formation of the rescue corridor into the driving

process. From the third level onwards, the vehicle

moves autonomously on the road. From this level on,

an emergency corridor can be automatically built by

the respective vehicle. In this work the simulation is

based on these parameters: the total number of lanes

on the motorway, the own lane number and the width

of the lane.

6 FUTURE WORK

The following questions also arise: What happens if

the autonomous vehicle breaks through the crash

barrier and enters oncoming traffic in the event of an

unforeseeable accident in the opposite lane? What

happens in the case of an unexpected deer crossing?

To answer these questions further implementations

and experiments are necessary. In this simulator the

lane detection is done using ray casts. Therefore, the

next step is the implementation of a lane detection

using video cameras (image recognition) as known

from practice. It is possible that not only sequential

programming but also the methods of machine

learning will be investigated. It would also be

interesting to see how the autonomous vehicles

behave when, for example, zipper procedures are

used on a construction site or driving onto a

motorway in flowing traffic. In the current

simulation, the vehicles drive at equal speed in each

lane and keep the safety distance to the following

vehicle. This is why the autonomous vehicles drive

side by side in each lane. This optimum uses the space

on the road, but this does not quite correspond to

reality. What will also be transferred to the simulator

and would be conceivable in practice is that each lane

has its specific speed. For example, on a three-lane

motorway, the autonomous vehicles drive up to 100

km/h in the right lane. Vehicles with speed between

101 and 119 km/h drive in the middle lane. From 120

km/h on, the vehicles drive in the left lane. The driver

of the autonomous vehicle can set this maximum

speed himself. Further experiments on this topic will

follow.

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