Automated Vehicle: a Review of Road Safety Implications as Driver of Change

Abstract

Road safety is a major issue in a number of countries around the world. Advanced technologies are in place to reduce the road hazards. These technologies include the overall automated vehicles (AV) as a part of an intelligent transportation system. They are a type of active safety systems.
This paper highlights the working mechanism, advantages and disadvantages of these technologies in the light of safety improvement, especially on highway or freeway facilities. This because in the early stage of AV implementation, traffic composition will be mostly manually-driven vehicles with several vehicles with automated driving functions and some AV. It becomes significant to study the key associated road safety issues when AV with different settings are mixed in traffic.
To address the aim of the research, it has been performed a wide recognition of the actual state of development about “autonomous and connected vehicles”, “on-road automated motor vehicles” or “fully automated car”. Indeed, all the literature and studies consider only theoretical approach but it was useful to do a strong meta-analysis in order to catch the overall coherence or influenced evaluation that characterize each kind of stakeholders.
An AV is able to increase capacity (C), the maximum service flow rate (MSFi), the adjustment factor for unfamiliar driver population (fP), passenger-car equivalent (PCE), or the proportion of heavy vehicles (PT). According to some authors, AV may offer much reduced perception and reaction time, tight car-following, precise lane keeping, correct assessment of gaps and crisp lane changing, no erroneous or unnecessary lane changes, and route familiarity. All of these issues face to a very strong AMF.
It would led to a new definition of the crash risk or crash taxonomy. Moreover, thanks to uninterrupted flow facilities, it has been set some factors describing drivers’ behaviors towards lane and shoulder widths, unfamiliar routes and lane change. However, AV is not sensitive to these factors due to its navigation, LIDAR and programmed car-following systems, so it was useful to discuss about a new type of AMF.
The AV technologies already represent the border of a new kind of road safety paradigm. Many features deal technical details and others face ethic or legal skills. It should be consider to make a review of all AV guidelines trough the several countries by the 2020 in order to take over the research effort needed.

Full text

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27th CARSP Conference,
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27ème Conférence ACPSER
Toronto, ON, 18-21 juin 201
Automated Vehicles: a Review of Road Safety Implications as
a Driver of Change
Tullio Giuffrè, Professor Eng.
Antonino Canale, PhD. Arch.
Alessandro Severino, PhD Student Eng.
Salvatore Trubia, PhD Student Eng.
University of Enna “Kore” (Italy), Faculty of Engineering and Architecture
Abstract
Road safety is a major issue in a number of countries around the world. Advanced technologies
are in place to reduce the road hazards. These technologies include automated vehicles (AV) as
a part of an intelligent transportation system. They are a type of active safety system.
This paper highlights the working mechanism, advantages and disadvantages of these
technologies in the light of safety improvement, especially on highway or freeway facilities. This
is because, in the early stage of AV implementation, traffic composition will be mostly manually-
driven vehicles with several vehicles with automated driving functions and some AV. It becomes
significant to study the key associated road safety issues when AV with different settings are
mixed in traffic.
To address the aims of the research, a wide review of the actual state of development of
“autonomous and connected vehicles”, “on-road automated motor vehicles” or “fully automated
car” has been performed. Indeed, while all the literature and studies consider only a theoretical
approach, it is useful to do a strong meta-analysis in order to catch the overall coherence or
influenced evaluation that characterize each kind of stakeholder.
An AV is able to increase capacity (C), the maximum service flow rate (MSFi), the adjustment
factor for unfamiliar driver population (fP), passenger-car equivalent (PCE), or the proportion of
heavy vehicles (PT). According to some authors, AV may offer much reduced perception and
reaction time, tight car-following, precise lane keeping, correct assessment of gaps and crisp
lane changing, no erroneous or unnecessary lane changes, and route familiarity. All of these
issues lead to a very strong Accident Modification Factor (AMF).
This leads to a new definition of the crash risk or crash taxonomy. Moreover, thanks to
uninterrupted flow facilities, some factors describing drivers’ behaviour towards lane and
shoulder widths, unfamiliar routes and lane change are established. However, AV is not
sensitive to these factors due to its navigation, LIDAR and programmed car-following systems,
so it is useful to discuss a new type of AMF.
AV technologies already represent the border of a new kind of road safety paradigm. Many
features deal with technical details and others face ethical or legal considerations. It is useful to
review all the AV guidelines developed in order to establish the research effort needed.

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Résumé
La sécurité routière est un problème majeur dans un certain nombre de pays à travers le monde.
Des technologies avancées sont en place pour réduire les dangers de la route. Ces
technologies comprennent des véhicules automatisés (AV) dans le cadre d'un système de
transport intelligent. Ils sont un type de système de sécurité actif.
Cet article souligne le mécanisme de travail, les avantages et les inconvénients de ces
technologies à la lumière de l'amélioration de la sécurité, en particulier sur les autoroutes ou les
autoroutes. C'est parce que, au stade précoce de la mise en œuvre de l'AV, la composition du
trafic sera principalement des véhicules à moteur manuel avec plusieurs véhicules avec des
fonctions de conduite automatisées et certains AV. Il devient important d'étudier les principaux
problèmes de sécurité routière associés lorsque AV avec différents paramètres sont mélangés
dans le trafic.
Pour répondre aux objectifs de la recherche, un large examen de l'état actuel du développement
des "véhicules autonomes et connectés", "véhicules automobiles automatisés en route" ou
"voiture entièrement automatisée" a été réalisé. En effet, bien que toute la littérature et les
études considèrent seulement une approche théorique, il est utile de faire une méta-analyse
forte afin d'attraper la cohérence globale ou d'influencer l'évaluation qui caractérise chaque type
d'intervenant.
Un AV peut augmenter la capacité (C), le débit de service maximal (MSFi), le facteur
d'ajustement pour la population de conducteur inconnue (fP), l'équivalent de voiture de
passagers (PCE) ou la proportion de véhicules lourds (PT). Selon certains auteurs, AV peut
offrir une perception et un temps de réaction beaucoup plus réduits, un suivi serré de la voiture,
une gestion précise des voies, une évaluation correcte des lacunes et un changement de voie
croquante, pas de changements de voie erronés ou inutiles et la familiarité des itinéraires.
Toutes ces questions entraînent un très fort Facteur de Modification des Accidents (AMF).
Cela conduit à une nouvelle définition du risque d'accident ou de la taxonomie des accidents. De
plus, grâce à des installations d'écoulement ininterrompues, certains facteurs décrivant le
comportement des conducteurs face aux largeurs des voies et des épaules, des routes
inconnues et des changements de voie sont établis. Cependant, l'AV n'est pas sensible à ces
facteurs en raison de sa navigation, de son LIDAR et de ses systèmes de conduite programmés,
il est donc utile de discuter d'un nouveau type d'AMF.
Les technologies AV représentent déjà la frontière d'un nouveau type de paradigme de sécurité
routière. Beaucoup de fonctionnalités traitent des détails techniques et d'autres font face à des
considérations éthiques ou juridiques. Il est utile d'examiner toutes les lignes directrices AV
élaborées afin d'établir l'effort de recherche nécessaire.
KEYWORDS: automated vehicle, driver behavior, road safety, traffic micro simulation.

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INTRODUCTION
The development of urban sustainable design models in the field of transportation is becoming
more connected to social sciences and technologies or internet practice, as well-known as
Information Communication Technologies, applied on road infrastructures [1]. In fact, starting
from this analysis, it should be possible to develop solutions with the purpose of implementing
and inducing more safe behavior regarding the driving experience.
It is often argued that the choice of driving behavior depends on the on-road circumstances and
on the current infrastructure. Also, habits are a key element for defining mobility models, and this
makes individual behavior changes very difficult to implement. The diffusion of Automated
Vehicles (AVs) will surely change the life style of the people. They will have benefits for disabled
and elderly persons. Van der Waeder et al. studied how to provide some insight into the factors
that can be used to stimulate car drivers to switch to carpooling, focusing on the home to work
trip [2].
Google reports more than 500,000 miles of testing of AVs on public highways, while several
auto manufacturers have announced the release of AVs within the next five years.
There is also considerable discussion and speculation on the influence that AVs can have on the
way we travel, and on our transportation systems. Some believe that AVs can potentially
transform our lives and our transportation systems in the near future [3], while others provide a
cautiously optimistic picture and present a long way ahead (several decades) before the many
benefits of AVs can be fully realized [4].
The potential benefits of AVs include, but are not limited to (I) increased highway safety due to
the elimination of human error in driving, assuming that AVs will not be subject to system failures
and abuse, (II) better use of traveler travel time for productive work or leisure, (III) independent
mobility for older adults, the disabled, and other mobility-constrained population segments, (IV)
reduction in fuel consumption and emissions due to smoother acceleration/deceleration
characteristics and improved traffic flow characteristics, and (V) increased road capacity and
reduced congestion. Moreover, the implementation of AVs will represent a step change in how
vehicles operate on the transportation network. Traditional models of driver behavior and
response to behavioral stimuli may not apply, and consequently our understanding of how traffic
and transportation systems work must be readdressed.
AVs capability and deployment on the (UK) road network [5] will impact observable vehicle
dynamics and “behavior” in many possible ways:
- profiles of acceleration and deceleration;
- signal lost time;
- development of platoons and road trains;
- the interaction with legacy vehicle fleet;
- lane positioning and vehicle alignment; and,
- vehicle headways and gap acceptance.
Changes to these characteristics will combine to influence road capacity, safety, journey time
reliability, emissions and other categories affected. Due to the multiple factors, and complex
mechanism of action, it is not possible to definitively state how these will positively impact safety
or redefine road crash modification factors.

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This paper, investigates the AVs revolution on mobility demand and on reimagining the road
network, through the management of micro simulation models applicable to AVs. The major aim
of traffic micro simulation is to produce representative measures of macroscopic traffic flow–
delay, journey time, flow and speed. The behavior of individual vehicles is not generally
considered in detail. As such, the fidelity of the base situation – including, for example, the
longitudinal spacing of vehicles, does not fall under scrutiny, and is not subject to site-specific
calibration or validation.
Particular Planung Transport Verkehr - Verkehr In Städten SIMulationsmodell (VISSIM)
parameters have been identified as levers – allowing the modification of vehicle behavior to
represent a plausible future for AVs. Behavioral change in VISSIM refers to a specific vehicle
type [5] [6].
This research will also show how the mechanisms by which AVs could impact traffic flow,
network performance and road capacity are, in the main, reasonably well understood and
broadly accepted. We will explore the impact of potential behavioral changes relating to:
- changed longitudinal movement of vehicles;
- the ability to change following behavior based on the capability of the lead vehicle;
- different levels of gap acceptance and lane changing behavior; and
- connectivity to represent better provision of inform decision making.
1. Recognition of the actual development of AVs
During the last decade, the subject of AVs gained strategic importance and widespread
relevance. Many projects were launched worldwide aimed at analyzing the first prototypes of
both vehicles equipped with automatic driving facilities, and road infrastructures supporting
these functionalities, testing and demonstrating their capabilities to the public. Brogi et al. wrote
about the history of intelligent vehicles, and discussed some of the different approaches
developed worldwide by a large number of research institutions [7].
One of the most important achievements is the vehicle safety integration system, using image
processing technology, that has been proven to reduce crashes significantly [8]. In particular, the
application of digital (CCD/CMOS) cameras used for driver assistance systems is the most
popular, being applied to lane departure warning and forward collision warning systems. In these
two applications, the most important components are lane boundary and preceding vehicle
detection, for which many methods have been developed.
For example, the inverse perspective mapping (IPM) approach is adopted to generate a bird’s
eye image of the road plane so as to remove the perspective effect and extract lane markings
through some constraint of road geometry. For vehicle recognition, some researchers applied
feature-based approaches to detect the preceding vehicle. By using particular vehicle features
appearing in a roadway image, such as texture, edge, symmetry and underside shadows, it is
possible to distinguish a preceding vehicle in the roadway image data.
Some studies used template-matching methods for detection by means of numerous vehicle
templates (such as edges, wavelet characteristics, etc.) built into the system [9]
The main disadvantage of these approaches is that a change of environment would affect the
recognition rate and easily cause misjudgement.

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Moreover, most of methods using learning algorithms are too complicated to implement the
image processing in real-time. The proposed vision-based driver assistance system not only can
reduce the noise interference from the roadway image but also can utilize the settings of
dynamic region of interest (ROI) to enhance real time processing. When the vehicle begins to
drift towards an unintentional lane change, or too close to the preceding vehicle, the system will
issue early warnings, with sound and light, notifying the driver to correct the driving direction or
to slow the vehicle down to prevent a potential collision.
Studies have proved that automated driving systems have the potential to decrease traffic
congestion by reducing the time headway (THW), enhancing the traffic capacity and improving
the safety margins in car following [9]. These investigations considered the effects of AV on
driver behaviour and traffic performance. Findings discovered that conventional vehicles (CV),
i.e. human driven, which are driving close to a platoon of AV with short THW, tend to reduce
their THW and spend more time under their critical THW.
Additionally, driving AVs can reduce situation awareness and intensify driver drowsiness,
especially in light traffic. In order to investigate the influences of AV on traffic performance, a
simulation case study consisting of a 100% AV scenario and a 100% CV scenario was
performed using microscopic traffic simulation. Outputs of this simulation study reveal that the
positive effects of AV on roads are especially highlighted when the network is crowded (e.g.
peak hours).
A remarkable improvement has been described for an autobahn segment, with an increase in
the average density of 8.09% during p.m. peak hours in the AV scenario, while the average
travel speed increased relatively by 8.48% [6]. As a consequence, the average travel time
improved by 9.00% in the AV scenario.
Table 1 below identifies, incompletely of course, the main literature review in the field of AVs.

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Table 1 – Main research and studies about AVs
Authors
Document
Year
J. S. Bay, et al. [10]
Intelligent Navigation of Autonomous Vehicles in an Automated Highway System:
Learning Methods and Interacting Vehicles Approach
1997
Y. Yim [11]
A Focus Group Study of Automated Highway Systems and Related Technologies
1997
A. Brogi, et al. [7]
Automated vehicle Guidance: the experience of the ARGO Autonomous vehicle
1999
F. Ku
̈c
̧u
̈kay and J. Bergholz [12]
Driver Assistant Systems
2004
M.Parent [13]
New Technologies for Sustainable Urban Transportation in Europe
2006
T. Scho
̈nberg, et al. [14]
Positioning an autonomous off-road vehicle by using fused dgps and inertial
navigation
2007
Jing-Fu Liu, et al. [8]
Development of a Vision-Based Driver Assistance System with Lane Departure
Warning and Forward Collision Warning Functions
2008
B. schwarz [15]
LIDAR Mapping the world in 3D
2010
D. Fajardo, et al. [16]
Automated Intersection Control Performance of Future Innovation Versus Current
Traffic Signal Control
2011
A. Carpentier and E. Polders
[17]
Intelligent Transport Systems: In-vehicle systems: Intelligent Speed Adaptation
(ISA)
2011
M. Pavone [18]
Autonomous Mobility-on-Demand Systems for Future Urban Mobility
2012
T. Bellemans, M. S
̌RAML
CAS collision evaiding steering
2012
S. Ds Rathod [9]
An autonomous driverless car: an idea to overcome the urban road challenges
2013
L. D. Burns, et al. [19]
Transforming Personal Mobility
2013
J. T. Bamonte [20]
Drivers of Change
2013
T. Bellemans and M. S
̌raml
Activity cruis control
2013
D. Dana, et al.
Autonomous driving
2013
N. El Faouzi and L. A. Klein [21]
Data Fusion for ITS: Techniques and Research Needs
2014
O. Bervoets, et al. [22]
Autonomous vehicle.
2014
D. Papaioannoua and L. M.
Martinezb [23]
The role of accessibility and connectivity in mode choice. A structural equation
modeling approach.
2015
P. Van der Waerden, et al. [24]
Investigation of factors that stimulate car drivers to change from car to carpooling in
city center oriented work trips
2015
J. Dokic, et al. [25]
European Roadmap; Smart Systems for Automated Driving
2015
M.D.Yap, et al. [26]
Valuation of travel attributes for using automated vehicles as egress transport of
multimodal train trips
2015
F. K. Turnbull
Automated anAutomated and Connected Vehicles. d Connected Vehicles
2015
M. J. Anderson, et al. [27]
Autonomous Vehicle Technology A Guide for Policymakers
2016
E. Aria, et al. [6]
Investigation of Automated Vehicle Effects on Driver’s Behavior and Traffic
Performance
2016

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2. AVs technical details and effects on operational parameters
Despite research and industry outputs reached are already valuable, a requirement is pending
for more complete clarifications relating to operational performances (traffic flow law distribution
and road capacity) that AVs technology will led. Hence, it will have to look at the impacts of
different AVs technologies on vehicle operation and traffic flow.
There is therefore a need for a “system of systems” approach to understand how all the varying
factors – technology capability, penetration, legal aspects, driver behaviour of existing vehicles -
all interact in complex, interconnected ways, impacting not just capacity but demand, emissions,
safety and an array of other factors.
When considering the main AV technical and technology details, one needs to note that AVs
require a variety of special sensors, computers and controls like: automatic transmissions,
diverse and redundant sensors (optical, infrared, radar, ultrasonic and laser) capable of
operating in diverse conditions (rain, snow, unpaved roads, tunnels, etc.), wireless networks
short range systems for vehicle-to-vehicle (V2V communications, and long-range systems to
access to maps, software upgrades, road condition reports, and emergency messages,
automated controls (steering, braking, signals, etc.), servers, software and power supplies with
high reliability standards [4].
An understanding of this “new road design and management” process will be needed to debate
about new types of AMF.
2.1. The information acquisition systems: vehicle-road
AVs use several information acquisition systems for both vehicle-road and vehicle-vehicle
communication. These systems are essential for the proper operation of AVs in order to ensure
safety with the different active safety mechanisms. They adopt a new vision system, Light
Detection And Ranging “LIDAR” [9], that can measure the distance to, or other properties of a
target by illuminating the target with light, often using pulses from a laser. LIDAR uses ultraviolet,
visible, or near infrared light to image objects and can be used with a wide range of targets,
including non-metallic objects, rocks, rain, chemical compounds, aerosols, clouds and even
single molecules. A narrow laser beam can be used to map physical features with very high
resolution.
So, the road following system is based on the detection of the lane and any obstacles. Although
some systems have been designed to work on completely unstructured roads (without painted
lane markings) lane detection has been generally reduced to the localization of specific features
such as road markings painted on the road surface [7].
Sometimes, the lane detection also runs on a digital-signal-processing-based module called the
VisLab Embedded Lane Detector (VELD). This model is based on the elaboration of dark–light–
dark (DLD) transitions on an inverse perspective mapping (IPM) image [28]. The IPM
transformation allows us to remove the perspective effect from the acquired image, remapping it
into a new 2D domain, in which the information content is homogeneously distributed among all
pixels. A lower dispersion of vehicle operating trajectories will be obtained; so, there will be a
greater stability of the trajectory, with important effects on degradation of road surfaces and the
possibility to redesign the cross section standard dimensions for each road class.

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In order to have better night vision, the Adaptive Light Control (also called Advanced Light
Control) improves the illumination of the road in front of the vehicle by offering the driver an
optimal light pattern in nearly every situation [12].
One countermeasure that is gaining increasing attention is the use in-vehicle technology to
assist drivers keep to speed limits, or even prevent the vehicle from exceeding speed limits on
all roads at all times. This is known as Intelligent Speed Adaptation (ISA) [29]. It is based on
autonomous navigation and equips the vehicle with an on-board system using navigation (i.e.
GPS-based) to determine the speed limit at the current position. A local speed limit database is
kept in the on-board system, based on information from a central speed limit database. ISA has
the capacity for real time updating to include information on areas where speed limits should be
reduced due to weather conditions (rain, snow, ice, fog), or around crash scenes and road
works.
DSRC (Dedicated Short-Range Communications) is a short-range (less than 1,000 meters)
wireless service specifically created to be the wireless link for V2V and vehicle-to roadside
infrastructure. DSRC is intended to enable short-range wireless communications both between
vehicles and between vehicles and roadside infrastructure — especially to support safety
applications such as intersection collision avoidance [27]. The advantages in using these
systems are in the area of the active safety.
Adaptive cruise control has two main task: driving at the maximum possible velocity and
avoiding a collision with a vehicle ahead. These tasks are also performed by the human driver,
but ACC will take over these tasks from the driver. The reaction time of ACC is 0,1 sec, and thus
about 10 times faster than a reaction time of a human being. The acceleration is often limited to
± 3 m/s2. Sharper breaking should be performed by the driver. The accuracy of ACC used to be
not good enough at low speeds and thus not used under 60km/h and during stop and go
conditions. Current technology grow and can be used at low speeds. [30]. This technology likely
will improve the comfort of the driving experience. The data the sensor receives is send to a
digital signal processor and a longitudinal controller. The controller will eventually determine
what the engine or braking system needs to do (accelerate or decelerate)
The CACC (Cooperative Adaptive Cruise Control) system, is an expansion of ACC. This system
is the same as ACC but it also uses a wireless communication hardware to share vehicle and
travel information which are used to make speed decisions. Dedicated Short Range
Communication (DSRC) establish the wireless communication which works like wi-fi and a radio
signal. The system allows vehicles to communicate with each other and share information about
speed, location, braking, roadway alerts, accelerating and decelerating. The information is also
much faster than the regular ACC. The result of the system is that the information can be shared
between vehicles to improve the distance to and the speed before anticipating on a dangerous
situation [31].
2.2. AV safety issues as new pattern of road crash genesis
After the discussion about the technical details of AVs and their technologies, it is useful to go in
depth on the safety issues resulting from novel crash risk factors.
Usually the AVs braking system mostly depends on finding any obstacles. If it finds an obstacle,
it would slow down. It would also measure the speed and size of such an obstacle. The results
of this analysis help the vehicle to take a decision on when and how to brake. It first slows itself

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down gradually and, when it finds braking to be necessary, it applies the vehicle’s brakes
together with some additional tasks. It should tighten the seatbelt to ensure the safety of the
passengers, sound the horn to alert others and, in an extreme situation, it should issue a
warning signal to the driver to notify him about the situation.
Emergency Lane Assist (ELA) provides another alternative for eliminating false alarms and
misuse. This function will only try to prevent dangerous lane departure. The system monitors
adjacent lanes and, as long as there are no other vehicles approaching, the lane markings can
be crossed without ELA intervention. However, as soon as a lane change manoeuvre is
considered dangerous, for example due to an oncoming vehicle, a torque is applied to the
steering wheel in order to prevent lane departure [32].
In case a crash cannot be prevented, automatic emergency brake systems reduce the speed in
order to minimize the severity of the collision [33].
Starting from these new kinds of safety issues, Figure 1 tries to describe the variation of the
danger level depending on the percentage of AVs within the road traffic flow. In particular, it can
be noted that there will be different risk conditions than the actual situation (where 100% of the
vehicles are CV) when vehicles will be totally autonomous. In fact, the following crash factors
were detected:
- environmental road surface and weather condition;
- vehicle technical deficiency, inappropriate load;
- database deficiency;
- new situation;
- system failure;
- cyber-attack.
This group of crash risk factors is also related to the presence of the electronic components
interconnected by Wi-Fi, infrared or Bluetooth technology to ITS systems which will be present
in the future road network.
A further variation of the determination of danger level is linked to the perception and reaction
time that will be close to 0 seconds in AVs. This will result in a significant danger level reduction
as new road safety pattern.

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Figure 1 – Shift of driving danger level depending by AVs percentage within traffic flow
3. Effort needed to reach a new road design vision
An previous investigation [34] showed that in 93% of the crashes of a 2,258 road crash sample,
human error was a contributory factor, while research [35] revealed that 95% of road crashes
are partially, and 65% of them wholly due to human errors. Although these studies are
somewhat outdated, more recent studies, such as [36], still acknowledge their validity by citing
them. In addition, different studies in the past have collected some valuable statistics and data
about the usefulness of ADAS and automated driving [6].
So, AV advocates predict that AVs will provide significant user convenience, safety, congestion
reductions, fuel savings, and pollution reduction benefits. Such claims may be overstated. For
example, advocates argue that because driver error contributes to more than 90% of crashes,
self-driving cars will reduce crashes by 90% [37]. This includes reductions in system failures
(“death by computer”), cyber terrorism [38], offsetting behavior (road users’ tendency to take
additional risks when they feel safer; also called risk compensation), and rebound effects
(increased vehicle travel resulting from faster or cheaper travel) [4].

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As is well known, a specific Transportation Research Board commission [39] verified, modified
and developed 35 AMFs in order to help practitioners and researchers to both improve and
develop them.
All of these findings were obtained considering that the traffic flow is 100% composed of CVs.
Traffic micro simulation is then a good way to investigate how safety and operational traffic
characteristics will change when AVs circulate in the street and how to think about the outlook
for new AMFs [40].
Although the topic of AVs arouses more and more interest, the modelling of AVs in traffic micro
simulation software is still very complex and articulated. A specific function to simulate the
coexistence in the same road network of both driverless and traditional vehicles is not still
sufficiently implemented.
In this research a special effort was dedicated to looking for the best way to fill this gap,
obtaining satisfactory results with traffic micro simulation software "PTV Vissim" and its car-
following model, Wiedemann 99.In fact, VISSIM presents the possibility of the coexistence of
two or more car-following models in the same link. To do that, the first step is to create a new
driving behavior and to assign it to the Wiedemann 99 car-following model.
Then, the car following parameters must be set as shown in Figure 2 instead of the prefixed
values.
Figure 2 – Wiedemann 99 editable parameters in VISSIM
The same operation must be done with both queuing (Figure 3) and lane changing behavior
(Figure 4), [6])
Once the new driver behavior is set, the next step will be to create a new vehicle called AVs and
to assign it the new driving behavior.
Then, the mix of traffic flow must be set with the number of both CVs and AVs. The final result
will be to simulate both CVs and AVs in the same road link at the same time.
Pre-fixed values
Settings proposed

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Figure 3 – VISSIM parameters related to the queuing behavior
Figure 4 – VISSIM parameters related to the lane changing behavior
Pre-fixed values
Settings proposed

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CONCLUSIONS
There is considerable discussion and speculation on the influence that AVs can have on the way
we travel and on our transportation systems. Moreover, the implementation of AVs will represent
a step change in how vehicles operate on the transportation network. Traditional models of
driver behavior and response to behavioral stimuli may not apply, and consequently our
understanding of how traffic and transportation systems work must be readdressed
Many studies have proven that automated driving has the potential to decrease traffic
congestion by reducing the time headway (THW), enhancing the traffic capacity and improving
the safety margins in car following.
AVs will have great impact on our lives. They will make driving safer, more convenient, less
energy intensive and cheaper. AVs use several information acquisition systems for both vehicle-
road and vehicle-vehicle communication. These systems are essential for the proper operation
of AVs in order to ensure safety with the different active safety mechanisms.
A study of the danger level modification during the transition from traditional car traffic to total AV
traffic was carried out. Several new crash risk factors were found, like database deficiency, the
occurrence of a new situation, a system failure or a cyber-attack.
Micro simulation is a good way to investigate how safety and operational traffic characteristics
will be changed when AVs circulate in the street and how to think about the outlook for new
AMFs. Satisfactory results were obtained with the traffic micro simulation software "PTV Vissim"
and its car-following model, Wiedemann 99. In fact, Vissim presents the possibility of the
coexistence of two or more car-following models in the same lane. By splitting the Wiedemann
99 model, and appropriately modifying the parameters of one of the its copies, it is possible to
obtain the coexistence of CV and AV in the same road link at the same time.
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