Safety-based Platoon Driving Simulation with Variable

Environmental Conditions

Youngjae Kim

, Nazakat Ali

and Jang-Eui Hong

Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea

Keywords: Platoon Driving, Safety, Variability, VENTOS Simulation, Cyber-Physical System.

Abstract: In platoon driving, a group of autonomous vehicles drives by forming one platoon to achieve advantages such

as fuel efficiency and traffic congestion reduction. Ensuring the safety of such a platooning system is very

challenging due to unexpected driving conditions e.g., adverse weather and obstacles on the road. Therefore,

the safety of a platooning system should be guaranteed even in variable weather conditions. In this paper, we

investigate the platooning system's unexpected behavior due to adverse weather conditions and provide safety

guards to avoid potential hazards. Simulation techniques are essential to confirm that the designed safety

guards work correctly, because testing such systems in a real situation can be highly expensive. Therefore,

we extended VENTOS, an open-source platoon driving simulator to verify the provided safety guards, which

can prevent risks under diverse weather scenarios e.g., fog, rain, snow, etc. Our simulation results show that

the proposed safety guards for adverse weather conditions can enhance the safety of the platooning systems.

1 INTRODUCTION

Nowadays, autonomous vehicles have become one of

the emerging technologies, and they can be a standard

way of transportation in the near future (Bagloee et

al., 2016). An autonomous vehicle is a type of Cyber-

Physical System (CPS) that collects information

about the road environment using various sensors like

camera, radar, and LIDAR (Light Detection and

Ranging sensors), and then actuates through actuators

like engine and steering based on the information. An

autonomous vehicle that drives by itself is a safety-

critical system that can lead to significant hazards,

such as loss of life and injury, etc., if the safety of

such a safety-critical system is not ensured properly

(Kalra, 2017).

Several standards are published or being

developed to guide and ensure the safety of

autonomous vehicles. ISO 26262 standard (ISO

26262, 2018) addresses the safety associated with the

entire life cycle of all electrical-electronic equipment

mounted on a vehicle. Other standard ISO/PAS

21448 (ISO/PAS 21448, 2019) was published to

address the safety of the intended functionality. The

https://orcid.org/0000-0002-0011-1216

https://orcid.org/0000-0002-3875-812X

https://orcid.org/0000-0001-9786-7732

other standard ISO/SAE FDIS 21434 (ISO/SAE

FDIS 21434, n.d.) is under development to protect

vehicles from cybersecurity attacks.

Despite these efforts, it is very difficult to achieve

rigorous safety for autonomous vehicles (Koopman &

Wagner, 2016). Autonomous driving in a platoon is

even more difficult, in which several autonomous

vehicles are driven by forming one platoon with a

narrow distance between vehicles. In platoon driving,

member vehicles exchange information with each

other using V2X (Vehicle to Everything) wireless

communication so that each vehicle can grasp

surroundings and respond agilely. The platooning

system, a representative example of collaborative

CPS, is recently in the limelight due to several

advantages such as enhanced traffic throughput,

lower energy consumption, pollution reduction, and

so on (Jia et al., 2015). These benefits are due to the

narrow distance between member vehicles. The

narrow distance can be obtained by collecting real-

time data about the other vehicles in the platoon. This

is achieved by using the Cooperative-Adaptive Cruise

Control (C-ACC) technique (Milanés & Shladover,

2014; Xiao et al., 2017).

558

Kim, Y., Ali, N. and Hong, J.

Safety-based Platoon Driving Simulation with Variable Environmental Conditions.

DOI: 10.5220/0010584505580565

In Proceedings of the 16th International Conference on Software Technologies (ICSOFT 2021), pages 558-565

ISBN: 978-989-758-523-4

A number of studies have been conducted to

ensure the safety of platoon driving (Xu et al., 2014;

Rahman & Abdel-Aty, 2018). In particular,

variability occurred in complex road environments

can have great risks due to its difficulty in predicting

and reproducing the situation. The possible risks from

variability like changeable weather conditions must

be considered in the development phase of a platoon

driving application. Although the ISO/PAS 21448

standard addresses risks that may be arisen due to

environmental variability, it is hard to find relevant

previous research in the platoon driving domain.

In this paper, we investigate environmental

variability (e.g., fog, rain, snow, etc.) in platoon

driving in order to provide safety guards to reduce the

risks caused by the variability. Considering diverse

scenarios, we defined a number of safety guards to

ensure platoon driving safety, particularly in case of

an unexpected scenario. Also, validating the safety

guards with real vehicles on real roads requires high

costs due to limited environments and potential

accidents. Thus, it is necessary to validate them

through simulation. Now, several simulators are

available for platoon driving. However, they did not

consider risks such as diverse environmental

conditions and also did not reflect such safety

requirements. Therefore, in our work, we extend an

open-source platoon driving simulator, named

VENTOS (VEhicular NeTwork Open Simulator)

(Amoozadeh, 2015) (VENTOS, n.d.), to reflect the

safety requirements for variable environmental

conditions. We simulate and analyze the effects of

safety guards designed to reduce the corresponding

risks to the variable environments with platoon

driving case study.

2 VARIABILITY AND SAFETY

GUARD IN PLATOON DRIVING

The goals of platoon driving are to reduce fuel

consumption and traffic congestion. However, the

degree of achieving safety goals is highly dependent

on the distance between member vehicles in a

platoon. The smaller the distance, the more

aerodynamic drag is reduced. However, the smaller

distance may lead to safety challenges of stable

driving and collision avoidance, and also serious risks

in platoon driving can bring out the loss of life. The

variable environments can have a significant impact

on the safety of autonomous vehicles. In the

following subsections, we first categorize possible

variabilities in the autonomous platooning system

that may lead to a number of uncertainties. After the

classification, we focus on environmental variability

with a number of variable scenarios, and define safety

guards to avoid dangerous situations during runtime.

2.1 Variability in Platoon Driving

2.1.1 Variability in CPS Applications

We classified potential variabilities that may lead to

uncertainties in CPS applications as below (Ali et al.,

2020):

 Environmental variability

 Physical variability

 Spatial variability

 Temporal variability

Environmental variability refers to the

variabilities that affect the performance of sensors

and actuators of CPS, such as dense fog, heavy rain,

strong sunshine, snow, etc. The physical variability

can be occurred due to a diverse set of hardware

devices or due to heterogeneous communication

infrastructure. Spatial variability is the variability

caused by spatial interference of CPS by other CPS or

other near objects. And temporal variability is the

variability caused by unexpected time differences in

systems like response time delay, the overhead of the

system etc.

Table 1 lists typical examples of applying the

above classification of variability to platoon driving.

Table 1: Examples of variability in platoon driving.

Variability Type Examples

Environmental

variabilit

Fog, Ice, Heavy rain, Snow,

Strong win

, and Sunshine.

Physical

variabilit

Battery aging, Tire wear,

and LIDAR

ower degradation.

Spatial

variabilit

Distance from another vehicle,

and Garbage dumped on the roa

Temporal

variabilit

Communication response delay,

and Overhead of ECU in the vehicle.

2.1.2 Environmental Variability in Platoon

Driving

Environmental variability was included in the scope

of the ISO/PAS 21448 standard published in 2019.

This standard, also named SOTIF (Safety of the

Intended Functionality), is established to reduce the

risks that can be occurred without functional failure.

Table 2 shows the topics covered by the ISO/PAS

21448 standard.

Safety-based Platoon Driving Simulation with Variable Environmental Conditions

559

Table 2: Safety relevant topics addressed by the ISO/PAS

21448 standard (ISO/PAS 21448, 2019).

Source Causes of the hazardous event

System

▪ Performance Limitations or insufficient

situation awareness, with or without

reasonably foreseeable misuse.

▪ Reasonably foreseeable misuse, incorrect

human-machine interfaces.

External

factor

▪ Impact from car surroundings (other users,

‘passive’ infrastructure, environmental

conditions: weather, Electro-magnetic

interference, ...)

Although several factors are involved in

environmental variabilities, our investigation focuses

on the following variable elements to ensure safety in

platoon driving:

 Cloud: Cloud reduces the light intensity, which

can cause performance limitations on the

camera sensor.

 Rain: The road may be slippery, increasing

braking distance by rain. It reduces the

perception of the vision systems in object

recognition.

 Fog: Fog affects the camera vision system,

making it difficult to distinguish road

conditions and other objects.

 Snow: Snow increases braking distance by

freezing the road and hinders the correct

steering of the vehicle. Also, piled snow can

disturb the vision system by covering its lens.

 Cold Weather with Rain: Cold weather with

rain can make black ice (a.k.a. an assassin on

the road).

 Heatwave: Overheated Engines can cause a

fire.

 Strong Wind: Strong wind interferes with

vehicle controls especially when crossing

bridges or high-level roads.

 Strong Sunshine: Strong sunshine hinders the

detection of forwarding objects. And it can

cause performance limitation in vision systems.

2.2 Safety Guards for Platoon Driving

2.2.1 Types of Safety Guards for CPS

Applications

CPSs are a safety-critical system that requires safety

guards to prevent dangerous situations. These safety

guards can be classified into two; safety guards for

pre-identified hazardous situations and safety guards

for unidentified hazardous situations (Wu et al.,

2017). The first one is reflected in the system

specification and then becomes part of the intended

functionalities in the system. The ISO/PAS 21448

standard suggests continuous modification of

functions to avoid identified risks. However, it is

impossible to identify all possible risks at the design

time of the system. Thus, as the second one, we

provide the safety guards for the potential risks which

are unknown at design time or training time.

2.2.2 Safety Guards and Its Roles

Representative safety guards and their roles applied

to platoon driving are given as below:

 Slowdown: Slowdown of vehicle speeds is an

essential safety guard in almost cases under all

members slowdown simultaneously.

 Speed Up: In certain situations, speeding up

may be necessary to avoid rear-end collision.

 Lane Change: In certain situations, an accident

can be avoided by lane changing from a

hazardous lane.

 Distance Gap Adjustment: Increasing or

decreasing the distance gap between vehicles in

the platoon can help to get more safe distance or

achieve the goal of platoon driving.

 Platoon Splitting: In the case of a large size

platoon, communication can be a problem due

to signal coverage. Therefore, splitting the

platoon into smaller sizes can increase the

stability of the platoon.

 Dissolution: In certain situations, it can be

difficult to maintain platoon driving (e.g.,

malfunction of a participant vehicle in the

platoon). In this case, the platoon can be

dissolved to promote safety at the individual

vehicle level.

 Distance Expansion with Outsiders:

Increasing the distance between a platoon and

another platoon (or other vehicles) is an

essential task to ensure safety in platoon

driving.

 Propagation of Hazardous Situation:

Platooning vehicles can receive information

from RSU (Road-Side Unit) or other vehicles.

Meanwhile, vehicles can transmit traffic

information to other vehicles and RSU using

V2X communication.

 Emergency Alert Signal Operation: Sending

alert and blinking signals to surrounding

vehicles can prevent an additional hazardous

situation.

The real world is dynamic, therefore, predicting

all hazardous situations is impossible. Although state

ICSOFT 2021 - 16th International Conference on Software Technologies

560

machine is considered to design and analyze each

safety guard for the hazardous situation, it can cause

state explosion and further increase the complexity of

the system (Kress-Gait, 2011). Therefore, it is

efficient to design the safety guards in advance

without considering hazardous situations, then select

and apply appropriate safety guards in order to

counteract the specific situation.

3 EXTENSIONS OF VENTOS

3.1 VENTOS Platoon Driving

Simulator

VENTOS is an open-source simulator developed by

UC Davis University to support platoon driving.

VENTOS simulator is a combination of two open-

source simulators; the road traffic simulator SUMO

(Simulation of Urban Mobility) (Behrisch, 2011)

(SUMO, n.d.) and the network simulator OMNET++

(Objective Modular Network Testbed in C++)

(Varga, 2010) (OMNET++, n.d.). In the VENTOS

simulator, many kinds of platoon driving strategies

such as platoon merging, platoon splitting, and

leaving from the platoon are implemented well. In

particular, the TraCI (Traffic Control Interface)

included in SUMO provides a convenient interface

for simulation control from external.

However, VENTOS had been developed

primarily with considering functional requirements

only for platoon driving. Safety of platoon driving

was not considered in VENTOS development.

Therefore, it is inappropriate to simulate possible

risks and safety guards in the development of platoon

driving application, as it had been constrained that no

risks occur during platoon driving. Hence, this paper

extends the VENTOS by modifying its source code

so that it can be used for verifying the safety of

platoon driving. Such extensions should be utilized

the scenario-based verification for hazardous events

presented in the ISO/PAS 21448 standard.

3.2 Implementation of the Effects of

Variability

For the realization of our scenarios for safety guards,

the source code of VENTOS were modified, and

some other modifications were made to SUMO. This

subsection briefly explains every implementation or

modification that is performed to SUMO.

In VENTOS, each vehicle determines its behavior

through the planMove function. These determined

results of the planMove function are executed in the

updateState function for the actual run. The risk

imposed by environmental variability cannot be

considered to determine the motion of the vehicle.

Therefore, implementing variability in the

updateState function can lead to unexpected

movements of vehicles by external influences. In

particular, sensor problems can lead to unusual values

delivery to the ECU of the vehicle. In this case, the

vehicle makes unreasonable decisions that are not

suitable for the actual environment.

Determining acceleration and deceleration is the

most important action for platooning vehicles. The

acceleration or deceleration of a platooning vehicle is

decided in the function followSpeed within the

carFollowingModel. We added the unexpected

acceleration or deceleration by modifying the source

code of the function followSpeed.

3.3 Implementation of Safety Guards

in Platoon Driving

Safety guards for abnormal situations can be

implemented in the function planMove in SUMO that

determines the behavior of platooning vehicles.

Otherwise, it is also possible to implement safety

guards in VENTOS itself and provide it to SUMO

simulation via TraCI. This allows for the

implementation of more appropriate and diverse

safety guards, especially in the context of platoon

driving.

4 SCENARIO VALIDATION

To demonstrate that our simulation approach is useful

to verify the safety of platoon driving, we prepare a

scenario in which environmental variability factors

are considered as follows.

4.1 Definition of the Scenario

We define a scenario to validate our approach. As

shown in Figure 1, the eight same vehicles are driving

by forming a platoon on the expressway which has

two lanes in one direction. The red-colored vehicle is

the leader of the platoon and member vehicles of the

platoon are shown in gradational blue color. The

platoon is driving in the first lane of the highway with

a target speed of 25 m/s (90 km/h) and a time-gap of

0.7 seconds. The first lane is occupied for

autonomous vehicles or platoons recommended by

the Automated Highway System (AHS) (Fenton &

Safety-based Platoon Driving Simulation with Variable Environmental Conditions

561

Mayhan, 1991). The configuration of this platoon is

shown in Table 3.

Figure 1: Simulation Map.

Table 3: Platoon Configuration.

Vehicle ID

veh.0 veh.1 veh.2 veh.3 veh.4 veh.5 veh.6 veh.7

Depth 0 1 2 3 4 5 6 7

Roll

Leader Followers

Color

Red Deep Blue – – – – – – – – – – Light Blue

In our scenario (Figure 1), a vehicle in the dense

foggy area marked with a gray circle has low

visibility of the distance about 50 meters. Also, in the

middle of the foggy zone, there is a yellow vehicle

that has stopped due to a malfunction. This scenario

poses a serious safety concern if the leader could not

recognize the stopped car in the foggy zone. In this

case, an appropriate safety guard should be applied to

the leader vehicle to avoid the risks from the dense

fog.

4.2 Implementation and Results of

Hazardous Scenario

4.2.1 Implementation of Variability

To simulate the hazardous scenario, we inject the

dense fog effect into the VENTOS. The variable

frontGap, which means the distance to the preceding

vehicle, and used in the vehicle's speed determination

algorithm, is modified to recognize the presence of

foggy situation. An object laid more than 50 meters

away will be not recognized by the vision system of

the vehicle in our scenario.

4.2.2 Results and Evaluation of Hazardous

Scenario

Results: After implementing dense fog in VENTOS,

the results of the simulation are shown in Figure 2.

The explanation of each scene are as follows:

(1) Vision systems without fog recognition do not

identify any vehicle ahead. Until the distance to

the forward vehicle reaches up to 50 m, the

platoon leader continues to drive at a speed of 25

m/s without recognition of the yellow vehicle

that has stopped at the front location.

(2) As the leader vehicle approaches the broken-

down vehicle, the vision system suddenly

recognizes the vehicle and starts to suddenly

brake.

(3) However, due to not enough time to stop safely,

a collision has happened between the platoon

leader and the broken vehicle (Figure 2, marked

by white dots).

(4) Because of this accident, a series of five rear-end

collisions happened. However, the fifth vehicle

in the platoon succeeded in stopping without a

collision.

(5) After series of collisions, the platoon initiates the

platoon splitting maneuver for member vehicles.

The fifth vehicle acquires a roll of new platoon

leader, three following vehicles of the new

leader join the new group.

(6) The new platoon moves to the second lane for

continuous driving.

(7) The new platoon passes by the accident location.

(8) The new platoon returns to the first lane again to

continue platoon driving on the recommended

lane for platoon driving.

Figure 3 shows the analysis results of the above

scenario. The speed graph represents that the platoon

leader encounters the first collision in 42.6 seconds.

The front space gap graph represents zero distance

from the vehicle ahead at that moment. The leader of

the newly formed platoon begins to accelerate again

by changing lanes in 56.6 seconds, as shown in the

scenario of Figure 2 (6).

Evaluation: The dense fog caused a series of five

collisions by continuing to drive at high speeds

without recognition of the object in a dense foggy

area. Therefore, safety mechanisms are needed to

avoid potential collisions in such changeable weather

conditions on roads. In section 4.3, we implement the

safety guards for such kinds of scenarios and show

the avoidance of potential collisions.

4.3 Implementation and Results of Safe

Scenario

4.3.1 Implementation of Safety Guard

We implemented the safety guards for dense foggy

situations. The variable environmental situations such

as dense fog can be recognized by the enhanced

vision systems of the autonomous vehicle. Once the

dense fog is recognized, the safety mechanisms

should be initiated to avoid risks like collisions. We

provide a safety guard Slowdown that decreases the

speed of the leader vehicle to a safe speed. The speed-

ICSOFT 2021 - 16th International Conference on Software Technologies

562

down vehicle can stop at a short distance within

detection coverage of the radar sensor. Additionally,

if dense fog is recognized, a message is sent to the

platoon members via V2V communication in order to

turn on the emergency alert signal.

Algorithm 1 is implemented for the safety guards

in the VENTOS framework.

Algorithm 1: Safety guard for dense foggy situation.

1 if (fogRecognition):

2 fogWarn = true

3 turnOnEmergencyAlertSignal()

4 sendMsg(turnOnEmergencyAlertSignal)

5 setTargetSpeed(15)

6 else:

7 fogWarn = false

8 turnOffEmergencyAlertSignal()

9 setTargetSpeed(25)

The detailed explanations of Algorithm 1 are as

below:

From lines 1 to 5 of Algorithm, line 1 means the

actions that a foggy situation is recognized by the

vision system of the platoon leader. In line 2, it sets

the variable

fogWarn to be true.

In lines 3 and 4, the leader turns on its emergency

alert signals. Then it makes all follower vehicles in

the platoon turning on the emergency alert signals to

warn other vehicles via V2X communication.

In line 5, the platoon leader decelerates its target

speed to 15 m/s (54 km/h). It makes follow vehicles

in the platoon also decelerate accordingly.

The lines from 6 to 9 mean the actions of the

leader after escaping the foggy zone. When the leader

goes out of the foggy zone, it sets the variable

fogWarn back to false, and turns off emergency alert

signals, and returns the target speed to 25 m/s, then

accelerates to the target speed.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Figure 2: Simulation Scenes of Hazardous Scenario.

Figure 3: Simulation result of speed (left) and inter-vehicle distance (right) for hazardous scenario.

Safety-based Platoon Driving Simulation with Variable Environmental Conditions

563

4.3.2 Results and Evaluation of Safe

Scenario

Results: Simulation results with safety guards are

shown in Figure 4. We explain each scene one by one

as follows.

(1) When the vision system of the platoon leader

recognizes the fog, it turns on its emergency

Alert signal and decelerates to a low speed that

can be safely stopped in a short distance.

(2) Despite the sudden appearance of a broken-down

vehicle, the leader vehicle was able to stop safely

with a sufficient distance.

(3) After identifying the surrounding conditions of

the road, the entire platoon changes the lane to

the second one.

(4) The platoon bypasses the broken vehicle.

(5) The platoon returns to the first lane to keep the

platoon driving.

(6) After escaping the dense foggy zone, the platoon

begins to perform normal driving again as shown

in Figure 4 (6).

Figure 5 shows the analysis results of the vehicle

movement in the safe scenario. At the time 34.2

seconds, the platoon leader recognizes that it

encounters a foggy zone and begins to decelerate. The

platoon leader then maintains a low speed of 15 m/s

and then stops safely even if the broken vehicle

appears ahead suddenly. The platoon then changes

lanes in 51.7 seconds by identifying the surrounding

situation. Then the leader vehicle increases the speed

again to 25 m/s after escaping the foggy zone from

68.5 seconds.

Evaluation: The designed safety guards Slowdown

and EmergencyAlertSignalOperation were able to

conduct properly to prevent accidents occurring in

hazardous scenarios (dense fog). Such safety guards

can be used to avoid potential risks in a variable

environment.

5 CONCLUSIONS

In our work, we investigate how to reduce the risks

that may arise due to environmental variability in

platoon driving. For this purpose, we first investigate

environmental variability in platoon driving and

analyze the characteristics of safety guards to reduce

potential hazards. We also utilize VENTOS, an open-

source platoon driving simulator, to simulate diverse

scenarios reflecting environmental variability (e.g.,

fog, snow etc.) and proposed safety guards to avoid

the potential hazards at runtime. The findings in this

(1)

(2)

(3)

(4)

(5)

(6)

Figure 4: Simulation Scene of Safe Scenario.

Figure 5: Simulation result of speed (left) and inter-vehicle distance (right) for safe scenario.

ICSOFT 2021 - 16th International Conference on Software Technologies

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paper will greatly help to analyze the impact of

environmental variabilities on the safety of

autonomous platoon driving. And it can also support

safety engineers to develop realistic platoon driving

techniques.

In the future, we will conduct a study about the

real-time properties of safety guards. It is very critical

to satisfying the real-time constraints to support

spatial and temporal variabilities as well as

environmental variability in autonomous (platoon)

driving. Thus, we will research and develop time-

constrained safety guards based on simulation

techniques.

ACKNOWLEDGEMENTS

This research was supported by the National Research

Foundation of Korea (NRF) grant funded by the

Korea government (Ministry of Science and ICT).

(NRF-2020R1A2C1007571).

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