Deliverable D6.1
Technology Opportunities and Strategies towards Climate friendly trAnsport
FP7-TPT-2008-RTD-1
Coordination and Support Action (Supporting)
Deliverable D6.1 (WP 5 report)
Transport Infrastructure Capacity Assessment
(Intelligent Transport Systems and the Impact on Capacity)
ROAD PASSENGER TRANSPORT
National Technical University of Athens
Voula Psaraki-Kalouptsidi, Ioanna Pagoni
Dissemination level
Public PU X
Restricted to other programme participants (including Commission Services) PP
Restricted to a group specified by the consortium (including the Commission
Services)
PE
Confidential, only for members of the consortium (including the
Commission Services)
CO
Deliverable D6.1 i
Coordinator: Dr. Andreas Schäfer
University of Cambridge
Martin Centre for Architectural and Urban Studies, and
Institute for Aviation and the Environment
1-5 Scroope Terrace, Cambridge CB2 1PX, UK
Tel.: +44-1223-760-129
Fax: +49-341-2434-133
E-Mail: [email protected]
Internet: www.toscaproject.org
Contact: National Technical University of Athens
School of Civil Engineering
Department of Transportation Planning and Engineering
5 Iroon Politechniou Str, Zografou Campus
15773, Athens
Tel.: +30210-7721740
Fax: +30210-7724181
E-Mail: [email protected]
Internet: www.ntua.gr
Voula Psaraki-Kalouptsidi
Tel.: +30210-7721204
Fax: +302107722404
E-Mail: [email protected]
Ioanna Pagoni
Tel.: +30210-7721204
Fax: +302107722404
E-Mail: [email protected]
Date: 14.3.2011
Deliverable D6.1 ii
Contents
Contents............................................................................................................................. ii
Abbreviations .................................................................................................................... iii
Abstract..............................................................................................................................1
1 Introduction ...............................................................................................................2
2 Reference System Characteristics ................................................................................3
3 Technology Developments ..........................................................................................5
3.1 Driver Assistance Systems (DAS) ................................................................................. 5
3.2 Automated Highway System (AHS) ............................................................................. 7
4 Constraints on reducing GHG emissions.......................................................................9
4.1 Technological feasibility .............................................................................................. 9
4.2 Social Acceptability.................................................................................................... 10
4.3 User Acceptability ..................................................................................................... 10
5 Results...................................................................................................................... 11
5.1 Technological feasibility ............................................................................................ 11
5.2 CO2 emissions reduction and capacity improvement ............................................... 12
5.3 Costs for reducing CO2 Emissions.............................................................................. 16
5.4 Social Acceptability.................................................................................................... 21
5.5 User Acceptability ..................................................................................................... 23
5.6 Cost effectiveness analysis........................................................................................ 24
5.7 CO2 Mitigation Costs ................................................................................................. 25
6 Conclusions .............................................................................................................. 25
List of Tables..................................................................................................................... 28
List of Figures.................................................................................................................... 28
References........................................................................................................................ 29
Annex A............................................................................................................................ 34
Annex B ............................................................................................................................ 40
Deliverable D6.1 iii
Abbreviations
Abbreviation Description
ABS Anti Blocking System
ACC Adaptive Cruise Control
AHS Automated Highway System
CAS Collision Avoidance System
CVIS Cooperative Vehicle-Infrastructure Systems
DAS Driver Assistance Systems
DGPS Differential Global Positioning System
ESC Electronic Stability Control
GDP Gross Domestic Product
GHG Greenhouse Gas
GJ Gigajoules
GPS Global Positioning System
ICT Information and Communication Technologies
ISA Intelligent Speed Adaptation
ITIF The Information Technology and Innovation Foundation
ITS Intelligent Transport Systems
Km kilometer(s)
LCA Lane Change Assistant
LDW Lane Departure Warning
MJ Megajoules
m/veh meters per vehicle
LB Lower bound
Lt litre(s)
NAHSC National Automated Highway System Consortium
NEDC New European Driving Cycle
OEM Original Equipment Manufacturer
passengers/h/l passengers per hour per lane
pkm/vkm Passenger per vehicle-kilometer
PATH Partners For Advanced Transit And Highways
SAVE System for effective Assessment of the driver state and Vehicle control in
Emergency situations (EU-funded project)
Deliverable D6.1 iv
TEN-T Trans-European Transport Network
UB Upper bound
VAT Value Added Tax
veh/h/l vehicles per hour per lane
V2I Vehicle to Infrastructure
V2V Vehicle to Vehicle
WP Work Package
Deliverable D6.1 1
Abstract
The TOSCA project aims to identify promising technology and fuel pathways to reduce
transportation-related greenhouse gas emissions through midcentury. An important building
block of this project is the techno-economic specification of low-GHG emission transportation
technologies, which are input into a scenario analysis. TOSCA considers all major modes of
passenger and freight transport, along with transportation fuels and technologies capable of
enhancing infrastructure capacity. This report is thus one out of a number of such techno-
economic studies.
In TOSCA Work Package 5 (WP5), technologies that could be implemented in the European
road and rail infrastructure over the next 40 years are evaluated both for passenger and
freight traffic. This deliverable (D6.1) examines technologies for road passenger transport that
could enhance mobility through improving the existing infrastructure capacity and, as a
secondary effect, could impact vehicle fuel consumption and GHG emissions.
In this report two Intelligent Transport Systems (ITS) technologies are evaluated for road
passenger transport: Driver Assistance Systems (DAS) and the Automated Highway System
(AHS). These technologies are anticipated to lead to a more efficient use of the existing road
network, while potentially reducing GHG emissions generated by individual vehicles. To assess
these technologies, we first define a reference system, which consists of the average new
passenger car in the road network of the EU-27 countries. Reference system characteristics
include vehicle fuel consumption, CO2 emissions and costs for the year 2009. Relevant
infrastructure capacity levels are also considered. After describing the various opportunities
offered by these technologies, the major constraints for reducing GHG emissions are
identified. Evaluation results are presented in terms of the basic dimensions of technological
feasibility, social acceptability and user acceptability.
Acknowledgement
We would like to thank Professor Amedeo Odoni for his numerous and valuable comments
that significantly improved the report.
Deliverable D6.1 2
1 Introduction
European Union countries experience 7,500 kilometers of traffic jams every day, with 10% of
the EU’s road network affected by congestion [1]. The fraction of time spent tied up in
congestion is even higher. As much as 24% of Europeans’ driving time is spent in traffic
congestion at a yearly cost of about 1% of the European Union’s GDP (approximately €115
billion) [2]. (Other studies, like P. Kopp and R. Prud’homme [3], suggest significantly lower
economic losses of about 0.03% of the GDP). These costs can be attributed mainly to loss of
time, but also to increased accident rates and more severe levels of air pollution.
It has been long recognized that any increase in road capacity, achieved by constructing new
highways provides only temporary relief from traffic congestion, while imposing a high
financial and environmental cost. One of the promising solutions suggested for this problem
are Intelligent Transport Systems (ITS). ITS technologies maximize the capacity of a given
physical infrastructure, thus reducing the need to build additional highway capacity. Overall,
ITS can reduce congestion by as much as 20% or more [4]. ITS also enable transportation
agencies to collect the real-time data needed to measure and improve the performance of the
transportation system, making ITS the centerpiece of efforts to reform surface transportation
systems and hold providers accountable for results [4]. ITS could also contribute to reducing
transport-induced CO2 emissions. Transport is a growing emitter of CO2. Between 1990 and
2007, transport sector CO2 emissions from within the EU-27 countries increased by 35.6%.
During the same period, road transport CO2 emissions have increased by 28.5%. Road
transport currently accounts for 70.9% of all transport related CO2 emissions in EU-27 [5].
The term ITS is used to describe systems which utilize a combination of computers,
communication, positioning and automation technologies to improve the safety, management
and efficiency of terrestrial transport, while reducing its environmental impact. ITS incorporate
four essential components:
• Vehicles, which can be located, identified, assessed and controlled using ITS;
• Road users, who employ ITS, for instance, for navigation, travel information and their
monitoring capabilities;
• Infrastructure, for which ITS can provide monitoring, detection, response, control,
road management and administration functions;
• Communications networks, to enable wireless transactions amongst vehicles and
transport users.
ITS applications increase safety, improve operational performance, particularly by reducing
congestion, enhance mobility and convenience, deliver environmental benefits, boost
productivity and expand economic and employment growth.
According to Ezell [4], ITS applications can be grouped within the following categories: 1)
Driver Assistance Systems (DAS) support drivers in maintaining a safe speed and distance,
driving within the lane to avoid overtaking in critical situations. 2) Advanced Traveler
Deliverable D6.1 3
Information Systems provide drivers with real-time information, such as transit routes and
schedules, navigation directions, and information about delays due to congestion, accidents,
weather conditions or road repair work. 3) Advanced Transport Management Systems include
traffic control devices, such as traffic signals, ramp meters, variable message signs and traffic
operations centers. 4) ITS-Enabled Transportation Pricing Systems include systems such as
electronic toll collection, congestion pricing, fee-based express lanes, and usage-based fee
systems (e.g. based on vehicle miles traveled). 5) Advanced Public Transportation Systems
that, for example, allow trains and buses to report their position so passengers can be
informed of their real-time status (arrival and departure information). 6) Cooperative Vehicle-
Infrastructure Systems (CVIS), are an enabler of automated highway systems (AHS), where
driving is computer-controlled using wireless communications between vehicles and
infrastructure. AHS provides the vision of “driverless” vehicles moving under automated
control.
Within the context of the above ITS taxonomies this report focuses on Driver Assistance
Systems (DAS) and the Automated Highway System (AHS). DAS have a considerable history. In
the late 1980’s, in Europe, several car manufacturers and research institutes carried out a
series of projects, such as PROMETHEUS (PROgraM for European Traffic with Highest Efficiency
and Unprecedented Safety) and DRIVE (Dedicated Road Infrastructure for Vehicle safety in
Europe), in order to determine the requirements and design standards for a class of ITS
applications, such as traveller information, vehicle control and safety systems [6, 7]. With
regard to AHS, the original research was performed by a team from Ohio State University.
Their first automated vehicle was built in 1962, and is believed to be the first land vehicle to
contain a computer. In the context of the PATH project, a prototype AHS was tested in San
Diego in 1997 [8].
This study suggests that DAS applications can contribute to a small (about 8%) increase in road
capacity and a small (around 5%) decrease of CO2 emissions from passenger cars. AHS, on the
other hand, could provide a significant improvement in road capacity, ranging from 2 to 2.5
times the current infrastructure capacity. At the same time, vehicle fuel consumption and CO2
emissions could decline by about 15-25%, due to mainly reduced aerodynamic drag and a
smoother traffic flow.
The characteristics, benefits, barriers to implementation and related costs are discussed in the
following sections.
2 Reference System Characteristics
The reference system is chosen to be the individually driven average new passenger car in
Europe (in 2009). Fuel consumption, CO2 emissions, costs and average annual distance
traveled for passenger cars are derived from the TOSCA WP1 report for road passenger
transport [9] and are shown in Table 1.
Deliverable D6.1 4
Table 1 Reference System Characteristics
REFERENCE PASSENGER CAR
Occupancy Rate pkm/vkm 1.5
Fuel type Petrol
Fuel Consumption lt/100km 6.2
CO2 Emissions gr/km 145
Retail Price €(2009) 16,500
Capital Costs €(2009)/year 660
Depreciation €(2009)/year 1,650
Operating costs (excl.fuel) €(2009)/year 1,260
Maintenance €/km
€(2009)/year
0.049
735
Parking and tolls €/km
€(2009)/year
0.013
195
Insurance €(2009)/year 330
Yearly Costs €(2009)/year 3,570
Average annual distance
traveled km 15,000
REFERENCE INFRASTRUCTURE CAPACITY
Road Capacity veh/h/l 1,800 [10]
Capacity passengers/h/l 2,700-3,600
REFERENCE ITS APPLICATIONS
Anti Blocking System (ABS)
Electronic Stability Control (ESC)
Table notes:
Vehicle retail price corresponds to the manufacture recommended price, in 2009, excluding VAT [9].
Maintenance costs include maintenance, replacement of parts, tires and service labour [9].
Insurance costs are assumed to be the 2% of the retail price of the vehicle [9].
The notions of occupancy rate, road capacity and reference ITS applications are discussed
below. The remaining concepts of Table 1 are presented in TOSCA WP1 report for road
passenger transport [9].
Occupancy rate depends on distance travelled. According to the 2001 U.S National Household
Travel Survey, for trips up to 50 km, it is slightly above 1.5 pkm/vkm, reaching 2 for distances
Deliverable D6.1 5
greater than 160 km and around 2.7 for distances greater than 1,000 km. In many European
countries, the occupancy rate of passenger cars ranges from 1.2 to 1.5 (pkm/vkm) [11, 12].
Road capacity is defined as the maximum sustained 15-min rate of flow, expressed in vehicles
per hour per lane (veh/h/l), that can be accommodated by a road under prevailing traffic and
roadway conditions in one direction of flow [10]. According to the Highway Capacity Manual
[10], highways can operate with capacities as high as 2,400 veh/h/l, under base traffic and
geometric conditions. In practice this cannot be achieved [12], resulting in lower capacities of
about 1800 veh/h/l. Road capacity issues are analyzed in detail in Annex A.
As indicated in Table 1, the reference passenger car is equipped with Anti Blocking System
(ABS) and Electronic Stability Control (ESC). These DAS applications are already standard in EU
countries. Some additional DAS applications have already been developed by Mercedes-Benz,
BMW, Volkswagen and Toyota but their deployment is still limited to premium cars. The
characteristics of the in-vehicle ITS applications and the current implementation status in
Europe are discussed in Sections 3 and 5 and in Annex A.
3 Technology Developments
This section describes ITS technologies that may be deployed in passenger cars and road
infrastructure by 2050. Emphasis is given to DAS and AHS technologies.
3.1 Driver Assistance Systems (DAS)
Driver Assistance Systems is a collective name for a whole range of Information and
Communication Technology (ICT) in-vehicle systems which support drivers in maintaining a
safe speed and distance, driving within a lane to avoid overtaking in critical situations. In
summary, they inform and warn the driver, provide feedback on driver actions, increase
comfort and reduce the workload by actively stabilising or manoeuvring the car. In TOSCA
WP5, we examine the following core systems:
• Anti Blocking System (ABS), which prevents the wheels of the vehicle from locking up
while braking;
• Electronic Stability Control (ESC), stabilizing the vehicle and preventing skidding;
• eCall, which automatically calls emergency services and transmits location data from
the scene of an accident;
• Adaptive Cruise Control (ACC), maintaining a preset distance to the vehicle ahead and
adjusting driving speed automatically;
• Lane Departure Warning (LDW) systems, warning the driver when the vehicle begins
to move out of its lane (unless a turn signal is on in that direction) on highways;
• Lane Change Assistant (LCA) systems, which continuously monitor the rear blind spots
on both sides of the vehicle;
Deliverable D6.1 6
• Intelligent Speed Adaptation (ISA), also known as Intelligent Speed Assistance, which
is a system that constantly monitors vehicle speed and the local speed limit on a road
and implements an action when the vehicle is detected to be exceeding the speed
limit;
• Collision Avoidance System (CAS), operating with a sensor installed at the front end of
a vehicle. The sensor scans the road ahead for vehicles or obstacles. When an obstacle
is detected, the system decides whether collision avoidance action is needed and a
manoeuvre is undertaken.
Most DAS applications that have been implemented in the market are based on purely in-
vehicle technology (e.g. ABS, ESC). However, other DAS (such as ISA) require some
components outside the vehicle, for instance for precise vehicle positioning. It is expected that
future DAS applications will be based on a combination of in-vehicle and infrastructure based
technology. This will significantly increase the complexity of the implementation of these
systems.
All these technologies are expected to improve highway operation by providing drivers with a
more relaxing driving environment resulting in lower rates of accidents and congestion.
Response time of emergency services is cut drastically by eCall reducing fatalities by 5-10%
[14]. As a side-effect, eCall and other applications such as ESC and Intelligent Speed
Adaptation (ISA) can reduce traffic congestion because 15% of all congestion in Europe is due
to accidents [15]. Moreover, if eCall were to be deployed on all vehicles, it could trigger a
decrease in fatalities by 5-15% across EU-27 by 2020 and a reduction in severe injuries by 10-
15% [16].
Vehicle manufacturers, such as BMW and Mercedes, have already installed DAS applications in
their cars. Examples include Electronic Stability Control (ESC), Adaptive Cruise Control (ACC),
and Anti Blocking System (ABS). Vehicle-to-Vehicle (V2V) communications systems are also
under development [17, 18, 19]. Data for the deployment status of DAS applications is given in
Annex A. Table 2 summarizes the historical market introduction of key DAS technologies.
Table 2 Introduction of DAS applications in the EU-market
DAS
application
Market Introduction in
EU (a)
Requirements
(b) Diffusion
(c)
Anti Blocking
System (ABS)
1978 (standard
equipment in Europe)
Wheel speed sensors,
Brake system interface
All new European vehicles
Electronic
Stability
Control (ESC)
1999
(wide penetration in
Europe)
Sensors,
Interface to the braking
system
All OEMs, but not in all lines
By 2012-2014 standard equipment
(according to EC2008 regulation)
Deliverable D6.1 7
eCall 2003
(for premium cars)
Link to emergency
services for position of
crashed vehicle and
identity
Partially on market: BMW, Lexus.
Optional but may become
mandatory
Adaptive Cruise
Control (ACC)
2005 Direct vehicle to vehicle
communication
First installed in Mercedes-Benz
Optional as comfort function
On market: BMW, Audi, Lexus
Lane Departure
Warning (LDW)
2005 Lane recognition Optional equipment sold as safety
function. On market: on a number
of vehicle models from Citroen to
Mercedes-Benz.
Lane Change
Assistant (LCA)
2005 Lane recognition ,
Blind spot monitoring,
Rear sensing
Partially on market: Audi, Volvo-
warning and only small steering
force
Optional as comfort function
Intelligent
Speed
Adaptation
(ISA)
2006 (as warning
function-based on
digital maps)
After 2015 (as an
adaptive function)
Digital maps,
Positioning system,
Environmental data,
Information on
vehicle limits
Several systems for speed limit
advice are available
May become mandatory
BMW: already installed it
Mercedes: in few years
Collision
Avoidance
System (CAS)
After 2015 Predictive sensing Optional as safety function with
potential to become mandatory.
Table notes: (a) Sources: [20, 21, 22, 23, 24]
(b) Source: [23, 24]
(c) Sources: [17, 18, 19, 21, 23, 25]
3.2 Automated Highway System (AHS)
Cooperative Vehicle-Infrastructure Systems (CVIS) are an enabler of the Automated Highway
System (AHS), where driving is computer-controlled using wireless communications between
vehicles and infrastructure. In automated highways, vehicles can organize themselves into
platoons and be linked together in communication networks, which allow the continuous
exchange of information about speed, acceleration, braking and obstacles. The first vehicle of
a platoon is called the platoon leader and its role is to manage the platoon and guide it on the
road. The other vehicles are called followers and their main goal is to maintain a specific
distance in time from the preceding vehicle using information from sensors. An AHS provides
the vision of “driverless” vehicles moved under automated control. It has the potential to
increase the capacity of existing highways (vehicles per hour per lane moving along the
Deliverable D6.1 8
highway) and thus reduce highway congestion. Since platooning enables vehicles operating
much closer together than is possible under manual driving conditions, each lane can carry
roughly twice as much traffic as it can today. Figure 1 shows the eight-car platoon,
demonstrated by the National Automated Highway System Consortium (NAHSC) in San Diego
in 1997.
Figure 1 Configuration of an eight-car platoon [26]
An AHS includes three different control systems. A longitudinal control system maintains
speed and spacing accuracy between the vehicles through the use of radar and radio
communication between cars. Each car in the platoon uses its radar to measure the distance
to the preceding car. The longitudinal control system enables short spacing between the cars
and thus increases highway capacity. What is more, the maintenance of a constant speed of
the platoon leads to a smooth ride. In addition, a lateral control system (or steering control
system) uses several technologies and aims to keep the vehicle within the dedicated lane. It
relies on sensors placed on the infrastructure and the vehicles. Each vehicle of the platoon
uses its sensors in order to determine its location with respect to the road markings and
geometry. Finally, a fault management system detects and handles failures in the sensors on
the vehicles. As a result, in case of a failure detected in any vehicle, the fault management
system is activated and controls the other cars of the platoon to avoid a crash.
Technology Trajectories
Figure 2 shows the envisioned technology trajectories for road passenger transport. DAS are
already present in the form of driver information systems providing guidance, warnings and
alerts to drivers. The next generation of DAS will enable automatic vehicle response to
warnings. Collision Avoidance Systems (CAS) fall in this category. In contrast, the AHS is
expected to emerge from the development of cooperative vehicle infrastructure (CVIS)
platforms that enable the exchange of information between vehicles as well as vehicles and
infrastructure. DAS systems will form an important component of AHS and will continue to
evolve. The vision of “driverless” vehicles moved under autonomous control is not expected to
materialize before 2030.
Deliverable D6.1 9
Figure 2 Technology trajectories for road passenger transport
4 Constraints on reducing GHG emissions
In this section, constraints on implementing the ITS technologies discussed above are
evaluated from three different aspects: technological feasibility, social acceptability and user
acceptability.
4.1 Technological feasibility
Within this area, challenges that need to be addressed include system interdependency,
network effect, application scale, funding, and political and institutional challenges. Many ITS
applications are interdependent, require system coordination to deploy, and must operate at a
sufficiently large scale to be effective. Regarding DAS, no significant technology constraints are
foreseen. AHS on the other hand must be integrated and at the same time adopted by
individual users to be effective.
There are two main requirements for the development and uptake of AHS, namely
interoperability and standardization. Interoperability issues should be addressed to enable
multiple services provided over multiple different platforms, which will work in different
countries (as vehicles can easily cross borders), while maintaining a simple-to-use interface
that requires minimum intervention from the driver. Standardization will eventually achieve
integration of vehicle and transport infrastructure. This requires open in-vehicle platform
architecture and safe human-machine interfaces, R&D advances in cooperative systems and
specifications for Infrastructure-to-Infrastructure, Vehicle-to-Infrastructure (V2I) and Vehicle-
to-Vehicle (V2V) communication. Due to the cross-border operation, interoperability and
standardisation issues have to be tackled at the EU level. Without Europe-wide coordination
and quality control, the functioning of any application in this field would depend on local or
national initiatives, and there would be a risk that customers, increasingly relying on such
support, will be confused when arriving in areas where the information is less reliable or even
absent.
Deliverable D6.1 10
4.2 Social Acceptability
Social equity implications: Since large amounts of public funds could be spent to deploy an
AHS, social equity issues must be addressed. A key question is whether it would be fair and
politically feasible to dedicate travel lanes to automated vehicles if many low-income drivers
cannot afford automated vehicles. Thus, concerns have been raised about creating two classes
of users-those who can afford to pay for AHS and those who cannot-with deleterious effects
for drivers who cannot pay.
Passenger Safety: The development of DAS and AHS significantly increases passenger safety.
However, negative safety consequences from ITS may also be encountered, as these systems
require additional equipment on the vehicle, such as in-vehicle screens, which may distract the
driver’s attention from his driving task [27]. When the “driverless” AHS vision was discussed in
the European Parliament, concerns and scepticism were raised regarding safety. During the
initial phase of ITS implementation, an adaptive period would be required so that drivers get
used to the new technologies. It should be completely clear for the driver when and how to
use various systems. Research on driver behaviour towards the use of these systems is under
way for the identification of the overall safety impact.
Privacy: Data privacy is a key factor for the future success of ITS technologies. Some of the
data will be personal data (for example, records of various payments) that may reveal user
identity. Other will be non-sensitive data such as velocity and length of trip. To avoid
unauthorised access and abuse of personal data, public authorities on national and
international levels should introduce privacy protection laws.
Liability issues: Liability issues have unique implications in the context of ITS technologies. As
the control of the vehicle is transferred from the driver to the “ITS elements”, an allocation of
responsibility might be made among the driver, vehicle manufacturer and the roadway
authority when an accident occurs. The legal community and the insurance industry will
undoubtedly be consulted extensively in considering the importance of legal and liability
issues associated with DAS and AHS technologies. It is clear that all parties involved, such as
drivers, manufacturers and roadway authorities, have a basic responsibility for safe traffic and
the use of ITS therein. A key question is how willing will car manufacturers be to accept the
potential liabilities involved in selling cars that drive themselves.
The resolution of data security and protection, and of privacy and liability issues may hinder
the uptake of certain advanced ITS services. These issues have been identified as being core to
the current slow uptake of ITS: in the absence of clear rules and responsibilities, neither
providers nor customers will be willing to invest or buy.
4.3 User Acceptability
A basic question in ITS implementation is user acceptability. Handing over control to a device is
evaluated as a negative aspect of DAS and AHS. According to a questionnaire survey
Deliverable D6.1 11
conducted in the SAVE project1 [28], drivers expressed concern about “loss of control” and
“driverless cars” which results in inconvenient driving. Drivers stated that they are reluctant to
release vehicle control and are willing to hand over control only in cases of emergency, such as
driver breakdown [28]. According to De Vos and Hoekstra [29], short spacing between vehicles
in platoons is also considered less comfortable and less accepted than larger headways
between vehicles [29]. However, the discomfort experienced due to short headways may
decrease after some time, as people get used to AHS driving. According to some research
findings from PATH program [7], once vehicle passengers have experienced 1 or 2 minutes of
AHS driving within a platoon, they become very comfortable with the experience.
5 Results
5.1 Technological feasibility
ITS are enabling technologies whose deployment is subject to a variety of conditions, including
maturity of technology, market acceptance or willingness to invest and buy, and is dependent
on actions being implemented in a coordinated way by various independent stakeholders.
Several DAS applications are already available on passenger cars and therefore the lower
bound in Table 3 is 2010 (see also Table 2). Following most examples of new vehicle systems,
initial fitment levels will be small and are likely to be in premium vehicles. The European
Commission has adopted an action plan suggesting targeted measures and an implementation
framework that will speed up their market penetration in Europe [20]. However, the full
potential of DAS will be realized only with large-scale deployment in vehicles. This requires
further regulatory actions. For example, the European Commission has proposed that all new
cars from 2012 on should have ESC systems, to drastically improve vehicle safety [21].
While the deployment of further DAS technologies is currently being pursued, the integration
of AHS requires major developments in two main directions: automation in vehicles and
automation in the road infrastructure. Although the first component already exists to some
extent the emphasis is on adapting and combining the separate ITS technologies. Initial
deployment and operation is expected to focus on high priority routes located in high demand,
major urban and inter-city corridors [30]. Table 3 summarizes the technological feasibility of
DAS and AHS for passenger cars.
1 The questionnaire of the SAVE Project was distributed to 407 drivers from 9 European countries.
Deliverable D6.1 12
Table 3 Technological Feasibility
Market-readiness R&D Requirements
Most Likely LB UB Insignificant
Significant
(company-level)
Substantial (EU-
wide program)
DAS 2015 2010 2020 X
AHS 2030 2025 2050 X
5.2 CO2 emissions reduction and capacity improvement
DAS technologies improve road capacity and reduce delays through speed and headway
adjustment. There are two ways in which congestion can be reduced when implementing DAS.
First, congestion reduction is a side effect of safety improvements. Usually, road accidents
impede the flow of traffic until rescue services have provided first aid to the accident victims
and the police have documented the incident. DAS limit accident severity and improve the
efficiency of the rescue chain (e.g. eCall). Thus the accident site is cleared more quickly and
congestion is relieved. The second effect on congestion is due to improvements in the traffic
flow. Some DAS, especially those which have an impact on the longitudinal control of the
vehicle, reduce variations in acceleration. As a consequence, the flow of traffic is smoother.
When a substantial part of the total vehicle fleet is equipped with DAS, noticeable capacity
improvements may result.
For DAS, the direct effects on road capacity are expected to be small when penetration rates
are small [25]. The main reason for this is that the travel time changes at the network level are
mostly small (in the order of seconds) and hardly noticeable to drivers. Capacity improvements
of about 7-8% in comparison with manual driving conditions due to the decrease of headway
are reported by Zwaneveld et al [31] (depending greatly on the penetration rate and the
headway setting). If we consider that manual driving results in capacities of 1800-2400
veh/h/l, driving with DAS would achieve maximum capacities of 1900-2600 veh/h/l.
Nevertheless, a study by Minderhoud [32] indicated that DAS applications, such as ACC, offer
an increase of road capacity only under strict conditions on market penetration and use.
Furthermore, DAS can contribute to fuel consumption and CO2 emission reductions. Some DAS
applications (such as ACC and ISA) have a direct effect on CO2 emission reduction through
changes in speed, while other (such as eCall, LDW and LCA) reduce CO2 emissions indirectly by
alleviating the congestion caused by accidents (as explained above). For example, ISA is
expected to contribute to a reduction of fuel burn and CO2 emissions by 2-5%, as it can reduce
the severity of accelerations and decelerations and stop-and-go transients [24, 33, 34, 35]. The
expectations of the effects of ISA on the environment are based on the reduction and the
homogenization of driving speeds. For the ACC system, effects of 0.5-10% on fuel burn and
CO2 emission reduction are reported in the literature since the system maintains a constant
Deliverable D6.1 13
speed and unnecessary “speed-ups” and abrupt braking are avoided2 [24, 36, 37, 38]. On the
other hand, the implementation of eCall, LDW and LCA has an indirect effect on CO2 emission
reduction by avoiding accidents and so avoiding subsequent queue formation (reducing the
amount of congestion and the emissions produced) [24, 36]. These indirect effects depend on
the number of avoided accidents and the amount of avoided congestion. However, this is very
hard to estimate. Finally, there are other DAS applications, such as ABS, that are not reported
to have effects on fuel consumption and CO2 emissions. This is the reason why the Lower
Bound on fuel consumption and CO2 emission reduction by DAS shown in Table 4 is zero.
The deployment of AHS is expected to increase road capacity in two ways. The most significant
is by reducing distances between fully automated vehicles until they reach the minimal safe
distance (considering the controllers’ capabilities and the road condition). This results in more
vehicles in a given lane. In addition, the AHS concept stabilizes the traffic flow and provides
the vehicles with conditions of constant cruise speed. Thus, traffic equilibrium can be reached
avoiding stop-and-go operations and inefficiencies caused by inattentiveness, merging,
weaving, and lane changing [38]. The capacity benefits offered by AHS depend on the platoon
configuration. Key parameters include the platoon size and the inter-vehicle and inter-platoon
separations. Various platoon sizes have been reported in the literature ranging from 5 to 20
vehicles per platoon. To maximize throughput it is desirable to form platoons that are
reasonably long [39]. The vehicle mix is another factor that influences the potential capacity of
a platoon (see Figure 3). Introducing even small percentages of trucks or buses to the flow of
passenger cars can significantly reduce the achievable capacity because of the poorer
performance of heavier vehicles [40]. For example, when the mix consists of 93% passenger
vehicles, 6% trucks and 1% buses, maximum capacity can be up to 4,900 veh/h/l. Finally, the
capacity of the AHS is affected by the length of the trip operated in the platoons and the
frequency with which vehicles enter and exit platoons. Short trip lengths and frequent entries
and exits limit capacity improvements. Conversely larger journeys (for example, up to an hour)
and a large percentage of total journey spent in platoons offer higher benefits [13]. A study by
Randolph et al [39] argues that platoon configuration should ensure that platoons remain
intact for considerable distances and proposes sorting the platoons depending on the
destination.
2 The real effect of ACC on emissions strongly depends on the behaviour of the driver. A fuel-efficient
driver may not be surpassed by the DAS.
Deliverable D6.1 14
Autonomous: vehicles are equipped with ITS-technologies, but not vehicle-to-vehicle communication, Low
Cooperative: vehicles communicate with each other only in cases of emergency, High cooperative: vehicles
continuously communicate with each other.
Figure 3 Simulated road capacity for AHS operation [41]
Figure 4 shows the potential benefits of AHS as derived in [12]. It assumes a vehicle length of
5m and intra-platoon spacings of 0.1 seconds. Capacities of 8,000 veh/h/l are obtained when
the platoon size is 16 vehicles. The practical considerations discussed in Section 2 suggest
taking the 80% of the theoretical value as a realistic figure. Therefore, capacities of 6400
veh/h/l could be achieved for platoons of 16 vehicles. The highest capacity values are achieved
at the highest speed provided the platoon size is large. Higher speeds require larger headway
and thus lead to small capacity increase.
Deliverable D6.1 15
Figure 4 Lane capacity in relationship with speed and platoon size
Another study is the platoon demonstration designed by researchers at the California PATH
program in 1997 [8]. The vehicles were organized in eight-vehicle platoons and travelled at a
fixed separation distance of 6.5 meters and at a speed of 105 km/h (0.2 seconds). The AHS
capacity which represents a “pipeline” capacity was about 5700 veh/h/l. Consideration of
divergence from ideal conditions and disturbances from vehicles entering and exiting the
highway results in a 20%-25% reduction and leads to an effective throughput of about 4300
veh/h/l.
In summary, estimation of the expected throughput for an AHS is a challenging task and has
been the subject of several studies [8, 13, 24, 41, 42, 43]. These studies show very high
capacity improvements with capacities up to 8000 veh/h/l as opposed to manual driving
conditions where capacity levels would be approximately 1,800 veh/h/l (see Annex A).
The high improvements on capacity refer to the vehicles that operate in platoons. However,
platoon operation will reduce the congestion of the adjacent lanes as well, as a percentage of
the whole traffic will be moved from the manual traffic to the platoons. The entry and exit of
vehicles to and from a high-capacity AHS lane can be accomplished without adverse impacts
on adjacent freeway lanes by use of dedicated entry and exit ramps, while the transitions
between manual and automatic vehicle control can be managed through electronic “check-in”
and “check-out” processing at these ramps [7, 40]. Nevertheless, the overall congestion
reduction depends on the penetration level of the AHS operation.
On the other hand, whilst the use of dedicated traffic lanes may greatly improve the
performance of the car fleets which use them, the resulting impacts on other vehicles, due to
reduced road space, could well counteract this advantage.
Besides capacity improvements, AHS offers a noticeable reduction in travel time. The study [4]
reports that in vehicle information and communication technologies can reduce travel time for
long trips by an average of 20%. Analysis of the Long Island Expressway and the Capital
Deliverable D6.1 16
Beltway network near Washington DC led to travel time reductions by 38% to 48% [44].
Another study [45] finds that the implementation of AHS can cut travel time by 33% to 50%.
AHS offers increased safety and lower fuel consumption when compared with individually
manoeuvred cars. The reason is that the cars in the platoons are close to each other, and
exploit the resultant lower air drag. The longitudinal control system can also reduce the
severity of accelerations and decelerations and stop-and-go transients thereby reducing fuel
use and emissions. The estimated fuel burn and CO2 emissions reduction for platoon
operations is 15-25%, depending on the number of vehicles in the platoon and the spacing
between them [8, 46]. This reduction should only apply to roads over which AHS will be
implemented. When comparing AHS driving with manual driving operating at free-flow, the
average fuel consumption reduction is low. However, when comparing it with manual driving
under congested conditions, the average fuel consumption reduction is high, because of the
smoother traffic flow associated with AHS [47]. Therefore, it should be clear that the high
benefits of AHS on fuel burn and emissions reduction are achieved at congested areas. The
above remarks are summarized in Table 4. Fuel consumption and CO2 emissions reduction
estimates are based on the literature review. However, some of the findings were also verified
by the views of experts that were interviewed in the context of the TOSCA project (see Annex
B).
There is a direct and proportional relationship between fuel consumption and CO2 emissions.
An improvement in terms of fuel efficiency would be immediately translated into a reduction
of CO2 emissions and vice versa (when related to road traffic and fossil fuel) [48]. This is the
reason why Table 4 indicates the same estimates for fuel consumption reduction and CO2
emission reduction for each technology.
Table 4 Technology Characteristics
Capacity (veh/h/l) Fuel Consumption Reduction CO2 Emissions Reduction
Most
Likely LB UB
Most
Likely LB UB
Most
Likely LB UB
DAS 2000 1900 2600 5% 0% 10% 5% 0% 10%
AHS 4300 4300 6400 20% 15% 25% 20% 15% 25%
5.3 Costs for reducing CO2 Emissions
In this section the retail price and the yearly costs of the proposed technologies are assessed.
The retail price of the ITS-equipped car consists of the price of the reference car (as given in
Table 1) and the extra unit costs needed to implement the ITS elements. The yearly costs of
the ITS-equipped cars include capital, depreciation and operating costs.
Various studies [4] have estimated that the benefit-cost ratio of ITS may range from 6:1 to 9:1,
far above the addition of conventional highway capacity, which has a benefit-cost ratio of
Deliverable D6.1 17
2.4:1 [49]. For example, a study of a model ITS deployment in Tucson Arizona, consisting of 35
technologies was conducted in 2005 [4]. These technologies would cost $72 million to
implement while the average annual benefits to mobility, environment, safety and other areas
were estimated to be $455 million annually, meaning a 6.3:1 benefit-cost ratio [4].
When assessing the costs associated with the implementation of ITS, individual drivers are
considered to be the end users and thus would need to carry the costs. In this report the costs
refer to those associated with the car which is equipped with ITS elements.
A cost estimate of each separate DAS system is speculative because rarely these elements are
installed in the car as a stand-alone option. Instead, they are frequently bundled with other
technologies. The costs do not reflect the possibility of shared use by other ITS applications.
Instead, the conservative position has been taken that all electronic costs are attributed to
DAS devices. Table 5 provides indicative values of unit costs for DAS applications as specified in
the literature [23, 50, 51, 52]. These costs reflect the cost to the original equipment
manufacturers (OEM) plus a mark-up for covering implementation costs on the vehicles. The
retail price (what the end user faces when purchasing the application) would be higher
because it would also include profit margins. As a rule of thumb, it can be stated that costs
have to be multiplied by a factor of 3 to reflect the retail price of DAS applications [50, 52].
Table 5 Unit costs and retail prices for DAS applications
DAS systems Unit costs (€/vehicle) Retail Prices (€/vehicle)
Electronic Stability Control (ESC) 150 400
eCall 60 180
Adaptive Cruise Control (ACC) 80-160 250-450
Lane Departure Warning (LDW) 300 800
Lane Change Assistant (LCA) 150-300 450-900
Intelligent Speed Adaptation (ISA) 230 600
Table notes: The retail prices have been derived from the stakeholder analyses done by the eImpact
Project [52].
The equipment of AHS-equipped cars includes vehicle longitudinal and lateral control,
advanced steering and cruise control and systems needed for platoon operations. To assess
the retail price and the yearly costs for the AHS-equipped car, we used cost data from the
Research and Innovative Technology Administration (RITA) [51].
We note that the above cost estimate could be speculative as it depends on the assumptions
made about what components will have already been installed on the vehicles for other
purposes (such as DAS systems) and can be re-used for AHS as well, versus which components
are needed only for AHS. For example, GIS software is already used for navigation and some
DAS systems, and will be used even more in the future. The AHS may need a higher level of
detail in the database, so there could be some modest incremental cost. Sensors for lateral
Deliverable D6.1 18
and longitudinal control may have already been installed on vehicles for Lane Departure
Warning (LDW) and Adaptive Cruise Control (ACC) respectively, but some additional
capabilities may be needed for AHS in platooning, so there could be a modest incremental
cost. Consequently, we assume that a variety of ITS elements will probably be present on the
vehicles and most of the additional investment will be in software.
Table 6 presents the costs for AHS equipment, considering all the assumptions mentioned
above. Unit costs represent the costs for obtaining ITS components plus the costs for
integrating them on the vehicle.
Table 6 Unit costs and maintenance costs for AHS elements in passenger cars
Subsystem Element Unit costs (€/vehicle) Maintenance Costs
(€/vehicle/year)
Mean LB UB Mean
Communication Equipment 190 130 260 5
GIS Software 120 100 140 0
Sensors for Lateral Control 350 290 410 10
Sensors for Longitudinal Control 190 140 240 5
Advanced Steering Control 200 180 220 5
TOTAL 1,050 840 1,270 25
Therefore the retail price of the AHS-equipped car is the reference retail price (equal to
16,500€, given in Table 1) plus the mean value of unit costs shown in Table 6 (equal to 1,050€).
Yearly costs include capital cost, depreciation and operating costs. Capital costs can be
expressed by the equation:
Capital costs = r · I
where I denotes initial investment [€] and r discount rate (equal to 4%). Depreciation costs can
be expressed as:
Depreciation costs = I/n
where n is the life of the vehicle [in years] (equal to 10 years). The initial investment
represents the retail price of the AHS-equipped car. Thus, the yearly costs can be calculated as:
Yearly cost = r · I + I/n + O
where O denotes operating costs [€/year]. Operating costs include maintenance, parking,
tolls and insurance costs.
Maintenance costs of the AHS-equipped car (see Table 6) are estimated to increase by 25
€/year (mean value) due to maintenance of the extra ITS elements (in comparison with the
reference car). Toll costs are expected to increase with AHS services to account for the
additional AHS infrastructure investments and maintenance. An average increase by 10% of
Deliverable D6.1 19
tolls cost of the reference car (which is presented in Table 1) is assumed. Thus, parking and
tolls costs of the AHS-equipped car3 are estimated to be about 0.0143 €/km. Considering the
above, maintenance costs will be approximately 760 €/year, parking and tolls costs will be
around 220 €/year.
Insurance costs of AHS compatible vehicles are uncertain. On one hand, the accident rates are
expected to decline as a result of AHS operations, and thus lead to reduced annual insurance
costs for the AHS-equipped cars. On the other hand, liability issues remain legally uncertain
(see Section 4.2) which may result in an increase of insurance costs. Insurance costs are
assumed to be 2% of the retail price (as assumed for the reference car), resulting in 350
€/year. These computations are presented in Table 7. Lower and upper bounds are indicated
in Table 6. It is noted that operating costs do not include fuel costs.
Table 7 Operating costs after AHS implementation
Maintenance Parking & Tolls Insurance
Operating costs
€(2009)/year
Reference car 735 195 330 1,260
AHS
Most likely 760 220 350 1,330
LB 760 220 345 1,325
UB 760 220 355 1,335
The results of the retail price and the yearly costs of an AHS-equipped car are presented in
Table 8.
Table 8 Retail price and yearly costs of the AHS-equipped car
Retail Price
€(2009)
Capital costs
€(2009)/year
Depreciation
€(2009)/year
Operating
Costs
€(2009)/year
Yearly Costs
€(2009)/year
Reference car 16,500 660 1,650 1,260 3,570
AHS
Most likely 17,550 700 1,755 1,330 3,785
LB 17,340 695 1,735 1,325 3,755
UB 17,770 710 1,780 1,335 3,825
3 Parking and tolls costs are assumed to increase only because the toll costs increase. Parking costs are
assumed to remain constant after the AHS implementation.
Deliverable D6.1 20
There are large costs in the design, development, testing and production of the components
and subsystems that comprise AHS. This means that the prices that have to be paid by vehicle
buyers (end users) cannot come down to moderate levels until there is a substantial
production volume (unless the suppliers decide to subsidize the early adopters because they
are confident of a large return on their investment later). The dominant consideration in
achieving unit cost reductions therefore has to be increasing the number of vehicles to be
equipped each year. But, suppliers usually follow a principle, beginning with small volumes but
higher value users and then advancing step by step to higher volume users who will be able to
pay successively lower prices. The key challenge is ensuring that at each step along the way,
these vehicle users achieve sufficient benefits from the system to justify the price that they
have to pay.
Figure 5 presents the trends in the retail price of the ITS elements over cumulative production.
The assumption is that the learning rate of ITS elements follows that of other electronic
elements such as computers. The learning rate used is 1% after doubling the cumulative
production [53]. Hence, after producing ITS equipment for the first 200,000 vehicles, the
production costs would decline by about 20% from its original value.
Trends of Retail Price of ITS elements
50
55
60
65
70
75
80
85
90
95
100
0 200.000 400.000 600.000 800.000 1.000.000 1.200.000
Production Quantity
%
Figure 5 Trends in retail price of ITS elements over cumulative output
Table 9 gives an overview of the retail price of some DAS elements in the years 2010 and 2020.
A reduction of retail prices from 2010 to 2020 is expected. Prices of both ESC and LDW are
predicted to decline by 25%. The retail price reduction for eCall and ISA is 16.7% [52].
Deliverable D6.1 21
Table 9 DAS retail prices in 2010 and 2020
Retail Prices
System 2010 2020 Percentage change
ESC 400 300 -25%
LDW 800 600 -25%
eCall 180 150 -16.7%
ISA 600 500 -16.7%
We assume that the yearly costs of an AHS-equipped passenger car will increase over time at
the same rate as for the reference car. Capital, depreciation and insurance costs depend on
the retail price, so it is important to estimate the evolution of the retail price of the passenger
car. The retail price is assumed to remain constant over time, while maintenance costs and
parking and tolls costs are assumed to decrease by an annual rate of 1%. The trends of the
retail price and maintenance costs are based on EU historical data and are in line with TOSCA
WP1 report on passenger cars [9]. The results are presented in Figure 6.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2010 2015 2020 2025 2030 2035 2040 2045 2050
Yearl
y Cost
s (€
2009/y
ear)
m
Figure 6 Trends in yearly costs of an AHS-equipped vehicle
5.4 Social Acceptability
When implementing ITS technologies, social acceptability aspects have to be taken into
account. The implementation concept has to be accompanied by actions that reduce barriers
to adoption and acceptance. In the following paragraphs we analyse social aspects that must
be taken into consideration when deploying ITS in Europe. These include economic growth and
generation of jobs within EC, passenger safety and comfort and privacy issues. Other aspects
such as social equity implications and liability issues have already been discussed in Section 4.
Deliverable D6.1 22
Economic growth and generation of jobs within EC: ITS will be an important growth industry
over the next 25 years. Experts predict that the cumulative global market for ITS-related
products and services will reach €300 billion in 2017 [54]. A number of countries, including
South Korea, Germany, and Japan, view ITS as a key industrial sector, capable of generating
considerable export-led economic and employment growth [5, 55].
Investments in ITS applications will create technology sector jobs for engineers, electronics
technicians, software developers, and system integrators. According to some estimates, about
50% of ITS project spending will be for direct labour as compared to 20% for new highway
construction [56]. The U.S. Department of Transportation estimates that the field of ITS could
create almost 600,000 new jobs over the next 20 years. A 2010 ITIF study [4] found that a €6.5
billion investment in ITS in the United Kingdom would support approximately 188,500 new or
retained jobs for one year.
Passenger Safety and comfort: The development of DAS and AHS is reported to increase
passenger safety. Vehicle-based sensors can detect hazardous conditions and computers can
take actions faster than drivers can. V2V communication enables vehicles that encounter
problems to “warn” other vehicles to take action. The stability that ITS technologies offer to
the vehicle also lowers the acceleration and deceleration regimes of each vehicle, and thus
reduces the probability of collisions, often caused by unstable traffic flow leading drivers to
apply high decelerations [38]. On the other hand, there are some safety issues that could be
investigated when implementing ITS. Car drivers using ITS may have false expectations from
the assisting function of ITS elements. Some DAS technologies might generate unexpected
driver behaviour (less alertness and over-expectations) which could eliminate positive safety
impacts [57, 58].
Privacy: Privacy issues have already been mentioned in Section 4.2. A survey conducted within
the CVIS Project examined the end-user's perception of present and future DAS technologies
on road transport. Data privacy was one of the aspects examined. The results indicated that
when drivers were asked about data privacy issues, around 60% stated that they would not
use these systems in case they would invade their privacy. Only 23% of respondents did not
mind the fact that the systems could invade their privacy since they consider the systems very
useful. However, when only car data are involved (no personal data), 60% of respondents
would agree to be geographically tracked [59]. In general, 60% of European drivers4 declared
to be willing to collaborate with some restrictions, for instance, as long as no personal data are
involved [59].
Table 10 presents the results of social acceptability for DAS and AHS. The ratings are from “--”
which corresponds to “significantly worse than reference” to “++” which corresponds to
“significantly better than reference”, with “0” meaning “comparable to reference”.
4 The survey was distributed among 12 European countries (Norway, Croatia, UK, Switzerland, Germany,
The Netherlands, France, Portugal, Belgium, Spain, Austria and Italy).
Deliverable D6.1 23
Table 10 Social Acceptability of DAS and AHS
So
cia
l e
qu
ity
imp
lica
tio
ns
Ge
ne
rati
on
of
job
s
wit
hin
EC
Pa
sse
ng
er
Sa
fety
an
d c
om
fort
Pri
va
cy
Lia
bil
ity
DAS - to 0 + + 0 -
AHS -- ++ + -- -
5.5 User Acceptability
ITS related surveys [59] show that ITS applications are overall positively evaluated. However,
percentages decrease when drivers surveyed had to pay for these systems. The percentage of
acceptance decreases by at least 25% on average when the end user is asked about willingness
to pay for the systems [59]. Consequently price could be a restrictive factor for widespread
adoption of ITS among road users. Today most ITS systems can be found mainly in premium
cars (see Annex A for the penetration rates in Europe). Middle and lower income classes would
not benefit from any advances if applications are too expensive. Platoon operations save
travel time for traffic overall and particularly for those vehicles in the dedicated lane. The
benefit of AHS with respect to time savings is the time difference between the results with and
without AHS driving conditions. A study by Juan et al [60, 61] examined a case of a three-lane
motorway with a dedicated lane for AHS-equipped cars. Among the various benefits of AHS
driving, they examined the time savings due to AHS in comparison with manual driving, using a
microscopic simulation model. The results showed that time savings occur when at least 30%
of total highway traffic is served by AHS5.
Acceptability of the DAS and AHS to the end users is rated and presented in Table 11 (ratings
are from “0” corresponding to “no adoption” to “5” corresponding to “full adoption”).
5 This fact could be a barrier for acceptance of the technology at the beginning of the deployment of
AHS (where penetration level may be lower than 30%) making it difficult to get enough users to adopt
the technology to exceed the 30% level. Thus, there should be a subsidy-based policy to get enough
users to adopt the technology so that AHS does provide significant time savings.
Deliverable D6.1 24
Table 11 User Acceptability at given User Cost
User Acceptability at given User Cost
DAS 4
AHS 4
5.6 Cost effectiveness analysis
The cost-effectiveness of AHS technology compared to the reference car is studied as break-
even costs. The break even oil costs are calculated to balance the yearly costs of the AHS-
equipped car (AHS) with those of the reference system (REF). The alternative technology
examined here is the AHS. Break even oil costs are related to the break even fuel price as
follows:
CAHS + P’F ∙ FCAHS ∙ D= CREF + P’F ∙ FCREF ∙ D
where: CAHS : Yearly costs for the AHS-equipped car [€/year]
CREF : Yearly costs for the reference system [€/year]
P’F : Break even fuel price [€/lt]
FCAHS : Fuel consumption with the AHS-equipped car [lt/km]
FCREF : Fuel consumption with the reference system [lt/km]
D : Annual distance traveled [km/year]
At this stage it has been assumed that fuel prices are not subject to change over time. The
yearly costs for the reference and the AHS-equipped car are shown in Section 0. Yearly costs
for the alternative technology are estimated to be 3,785 €/year, while yearly costs for the
reference system are 3,570 €/year (see Table 8). The fuel consumption for the reference car is
given in Table 1 (equal to 6.2 lt/100km). For the AHS-equipped car, there is a 20% reduction in
fuel consumption (see also Section 5.2) resulting in 5 lt/100km. We assume that the average
number of km traveled annually is 15,000. About 35% of these trips are inter-urban and are
carried out on highways. An additional 15% corresponds to trips within urban areas over three
lane freeways which can accommodate AHS equipped cars. Based on these rough estimates
which greatly vary between countries, we assume that 50% of the annual mileage is
potentially done on roads over which AHS will be implemented. According to the above, break
even fuel price (P’F) is estimated to be about 2.39 €/lt.
Break even oil price is calculated using the following equation:
Oil Price = Fuel Price - Refining Costs - Distribution Costs - Taxes
Deliverable D6.1 25
where fuel price is the break even fuel price computed above. According to TOSCA WP4
Report [62], distribution costs are equal to 0.4 €/GJfuel. Refining costs and taxes are assumed to
be 3.7 €/GJfuel and 27.7 €/GJfuel respectively.
Considering that the Net Energy Content of the gasoline is equal to 32.2 MJ/lt = 0.0322 GJ/lt
break even fuel price is equal to 74,2 €/GJfuel. Therefore, the break even oil price is equal to:
Oil Price = 74.2 - 3.7 - 0.4 - 27.7 = 42,4 €/GJfuel
or 315 US dollars per barrel (1 Euro=1.3 US$).
5.7 CO2 Mitigation Costs
In this section, the costs to reduce 1 gram of CO2 emissions are calculated. The CO2 mitigation
costs are related to the yearly costs and the CO2 emissions for the reference system and the
AHS-equipped car. They are expressed by the following equation:
D)COCO(
CCC
)AHS(2)REF(2
REFAHS2CO
⋅=
-
-
where: CCO2 : CO2 mitigation costs [€/gr]
CAHS : Yearly costs for the alternative technology [€/year]
CREF : Yearly costs for the reference system [€/year]
CO2 (REF) : CO2 emissions generated by the reference car [gr/km]
CO2 (AHS) : CO2 emissions generated by alternative technology [gr/km]
D : Annual distance traveled [km/year]
The yearly costs for the reference and the AHS-equipped car have been estimated in Section 0
(see Table 8). CO2 emissions for the reference car are equal to 190 gr/km (Table 1). The AHS-
equipped car achieves a reduction of 20% (152 gr/km) in fuel consumption and CO2 emissions
as reported in Section 5.2. The above computation results in CO2 mitigation costs of about 7,55
∙ 10-4 €/gr (or 755 €/tn).
6 Conclusions
This chapter provides a techno-economic analysis that identifies some of the main issues
related to the deployment of Intelligent Transport Systems (ITS) in road passenger transport.
Driver Assistance Systems (DAS) and Automated Highway System (AHS) are evaluated. The
main focus of these technologies is infrastructure capacity improvement, but CO2 emissions
reduction is assessed as well. DAS include ITS technologies that support drivers in maintaining
a safe speed and distance, driving within the lane to avoid overtaking in critical situations. The
concept of AHS requires the use of vehicle sensing systems, inter-vehicle communication
systems, infrastructure sensing systems, and “cooperation” between vehicles and
infrastructure. DAS, AHS and more generally ITS applications hold good promise to provide
significant benefits: they would increase safety, improve the operational performance of the
Deliverable D6.1 26
transportation network, particularly by reducing congestion, reduce emissions and expand
economic and employment growth.
DAS are already present in the form of driver information systems providing guidance,
warnings and alerts to drivers. The next generation of DAS will enable automatic responses to
warnings. The full potential of DAS will become a reality only with large-scale deployment in
vehicles. This requires further regulatory actions. The AHS is expected to emerge from the
development of cooperative vehicle infrastructure (CVIS) platforms. The vision of “driverless”
vehicles moved under autonomous control is not expected to materialize prior to 2030.
AHS is the most promising technology for increasing capacity. This is achieved without having
to build new roads, by introducing “intelligence” in both the vehicles and the roadside. AHS
could also contribute to emissions reduction. However, very high benefits (both with respect
to capacity and emissions reduction) are achieved only at congested areas of application.
The majority of the technical issues regarding automated vehicle operation have been solved
to a great extent. However, there are several issues that need to be considered like
interoperability, standardization and data protection. In an advanced system such as AHS, the
driver will be confronted with significantly more information and possibly more controls than
those currently used in vehicles. The issue of drivers accepting devices which either tell them
what to do or take control away from them is nontrivial. Furthermore, distances between
vehicles are severely reduced at high speeds using longitudinal control and thus drivers may
feel unsafe in the automated vehicle. Societal and institutional issues such as liability are not
fully addressed yet and could delay AHS deployment.
A wide range of ITS technologies are currently at various stages of development but their
eventual widespread adoption will depend on acceptance by both consumers and
governments. Consumers may be willing to pay for AHS services on congested networks and
particularly on motorways with high demand levels where operating in platoons results in
significant time savings. However, there should be a regulatory framework for the introduction
of ITS applications in order to achieve their EU-wide implementation. Leaving the situation
unchanged would lead to stagnation or even deterioration of the current conditions regarding
the deployment of ITS, resulting in an unchanged low level of market take-up and making it
hard to achieve key policy objectives and, indirectly, to contribute to congestion reduction,
road safety and environmental nuisance. The responsibilities of the different players (EU,
public authorities, industry, etc) need to be clearly identified, while business cases including
public-private cooperation should be defined and a legal basis for actions established.
Uncertainties about legal responsibilities and consequences for regulatory regimes may
hamper mass development and market introduction of these systems in Europe.
Table 12 summarizes the main ITS technologies analyzed in this report and their key
dimensions.
Deliverable D6.1 27
Table 12 Most Promising Technology Scenarios
Reference System DAS AHS
Market readiness 2010 2015 2030 Technological
Feasibility R&D
requirements Insignificant Substantial
Capacity 1800 veh/h/l 2000-2600 veh/h/l 4300-6400 veh/h/l Technology
Characteristics GHG emissions
reduction 5% 20%
Retail Price 16,500 € 17,550 € Cost
Characteristics Yearly Costs 3,570 €/year 3,785 €/year
Social equity
implications - to 0 - -
Generation of
jobs within EC + ++
Passenger Safety + +
Privacy 0 - -
Social
Acceptability
Liability - -
User
Acceptability At given user cost 4 4
Cost effectiveness
analysis (Break
even oil price)
42,4 €/GJfuel (315
US$ per barrel)
CO2 mitigation
costs 755 €/tn CO2
Deliverable D6.1 28
List of Tables
Table 1 Reference System Characteristics .............................................................................. 4
Table 2 Introduction of DAS applications in the EU-market ................................................... 6
Table 3 Technological Feasibility........................................................................................... 12
Table 4 Technology Characteristics....................................................................................... 16
Table 5 Unit costs and retail prices for DAS applications ..................................................... 17
Table 6 Unit costs and maintenance costs for AHS elements in passenger cars.................. 18
Table 7 Operating costs after AHS implementation ............................................................. 19
Table 8 Retail price and yearly costs of the AHS-equipped car ............................................ 19
Table 9 DAS retail prices in 2010 and 2020 .......................................................................... 21
Table 10 Social Acceptability of DAS and AHS..................................................................... 23
Table 11 User Acceptability at given User Cost................................................................... 24
Table 12 Most Promising Technology Scenarios ................................................................. 27
Table 13 Overview of DAS penetration rates in premium cars in Europe........................... 38
Table 14 Penetration rates of DAS applications in Europe according to eIMPACT............. 38
List of Figures
Figure 1 Configuration of an eight-car platoon [26] ................................................................ 8
Figure 2 Technology trajectories for road passenger transport .............................................. 9
Figure 3 Simulated road capacity for AHS operation [41]...................................................... 14
Figure 4 Lane capacity in relationship with speed and platoon size...................................... 15
Figure 5 Trends in retail price of ITS elements over cumulative output................................ 20
Figure 6 Trends in yearly costs of an AHS-equipped vehicle.................................................. 21
Figure 7 Speed-flow relationship for a highway under ideal conditions [1] .......................... 35
Figure 8 Development of the TEN-T roads in EU-27 in 2005, 2013 and 2020........................ 35
Figure 9 Assessment of number of lanes for the TEN-T road network.................................. 36
Deliverable D6.1 29
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Road, Application Areas: Vehicle Safety Systems. Retrieved from:
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htm
Deliverable D6.1 30
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Intelligent Transport Systems in Europe and the Proposal for a Directive of the
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Deliverable D6.1 31
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Deliverable D6.1 32
http://www.itsbenefits.its.dot.gov/its/benecost.nsf/ID/80E55CF5EC701787852569610
051E267?OpenDocument&Query=Home
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%20Phase%201%20Report%20%28Final%29.pdf.
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Opportunities for Managing Transportation Performance through Technology.
Available on http://www.its.dot.gov/index.htm
Deliverable D6.1 33
57. Van der Heijden R., Van Wees K. (2001). Introducing Advanced Driver Assistance
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747.
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report, DBFZ, Leipzig.
Deliverable D6.1 34
Annex A
A1: Road Capacity Assessment
In this section, we evaluate the existing road infrastructure in EU-27, focusing on the Trans-
European (TEN-T) road network. The term “capacity” is used to quantify the traffic-carrying
ability of transportation facilities. In this report the definitions are mainly based on the
Highway Capacity Manual [1], the source most often used to estimate capacity. The capacity of
a facility is “the maximum hourly rate at which persons or vehicles reasonably can be expected
to traverse a point or a uniform section of a lane or roadway during a given time period, under
prevailing roadway, traffic and control conditions” [1]. Traffic flow on a highway is the product
of speed and density as given in the following equation [1]:
s(V)L
V1000Q
+⋅=
where: Q : Traffic flow [veh/h/l]
V : Speed [km/h]
L : Vehicle length [m/veh]
s(V) : Separation as a function of speed V [m/veh]
By definition, capacity is the maximum value of Q. The relationship between spacing and
speed (or the so-called “separation policy”) is decided by human driving habits. Under ideal
traffic and geometric conditions, capacities can be as high as 2,400 veh/h/l (see Figure 7). This
capacity is achieved on highways with free-flow speeds of 120 km/h or greater. In practice,
this capacity cannot be achieved due to the difference between the prevailing conditions and
the ideal ones. For example, the theoretical capacity depends on the separation and speed of
vehicles if they continue along with no disturbance. In reality, there will be disturbance
between the vehicles in the highway (when entering or leaving a lane), resulting in lower
capacities [2].
As indicated in Figure 7, the point at which an increase in flow rate begins to affect the
average passenger car speed varies from 1,300 to 1,750 veh/h/l. For highways with free-flow
speeds of 90 km/h, speed will decline when capacity reaches 1,750 veh/h/l. For higher free
flow speeds, the average speed begins to decrease at lower flow rates. Plots for any other
value of speed can be obtained by interpolation.
Deliverable D6.1 35
Figure 7 Speed-flow relationship for a highway under ideal conditions [1]
The TEN-T road network comprises motorways and high-quality roads, whether existing, new
or to be developed which:
• play an important role in long-distance traffic, or
• bypass the main urban centres on the routes identified by the network, or
• provide interconnection with other modes of transport, or
• link landlocked and peripheral regions to central regions of the Community.
The total length of the TEN-T roads including motorways and high-quality roads (but not
including ordinary roads) in the EU-27 in 2005 was approximately 70,200 km. According to
some estimates, the total length of the TEN-T roads will be about 85,000 km in 2013 and about
90,500 km in 2020 [3, 4]. These estimates are presented in Figure 8.
0
20000
40000
60000
80000
100000
120000
Len
gth
(km
) J
Total 98505 99888 100346
Motorway 48186 59201 63125
High quality road 22002 25683 27375
Ordinary road 28317 1500 9845
2005 2013 2020
Figure 8 Development of the TEN-T roads in EU-27 in 2005, 2013 and 2020
Deliverable D6.1 36
The characteristics of TEN-T roads, including the number of lanes and length, were collected
from various sources. Figure 9 presents the number of lanes of the European road network.
More than 80% of the network include highways with three or more lanes (per direction) and
can accommodate more than 5400 or more vehicles per hour per direction. Also, 6% and 14%
of the TEN-T network can accommodate up to 1800 to 3600 vehicles per hour per direction
respectively. Capacity data for specific roads are not generally available, so the definition of
highway maximum capacity is adopted (1,800 veh/h/l). This value provides a first order
approximation of road infrastructure capacity in Europe. The high percentage of highways with
more than 3 lanes (approximately 80%), indicates that European highways can accommodate
AHS systems by dedicating one lane to platoon operations.
Figure 9 Assessment of number of lanes for the TEN-T road network
Deliverable D6.1 37
A2: Deployment status of ITS in Europe and around the world
− Electonic Stability Control (ESC)
ESC has been on the market since 1999 and is standard equipment in many cars of the middle
and upper price classes, but not yet in smaller cars. Sweden has been foremost in the national
promotion of ESC and in 2006 over 90% of new cars sold in Sweden were fitted with ESC [5].
−−−− eCall
A road map for eCall deployment has been established and agreed by the eSafety Forum.
eSafety partners (European Commission, industry, public authorities and other stakeholders)
have agreed to introduce eCall as standard equipment in all vehicles entering the market after
September 2010 (i.e. models of the year 2011) [5]. eCall was initiated by the private sector
(automobile industry) but relies on wider cooperation among key stakeholders for real
operational use. Ongoing discussions on the overall service chain, the institutional framework
(who will handle the incoming messages), the overall benefits and the related necessary
investments (who will/ should pay for safety) are seriously affecting the initial timeline [6].
− Intelligent Speed Adaptation (ISA)
In recent years, ISA technology has spread around the world. Different tests using informative
and supportive systems across Europe have shown that approximately 60-75% of users would
accept ISA in their own cars [5]. Several car manufacturers have installed as standard outfit in
some of their car types manual ISA, i.e. the driver can set a desired speed limit. Then driving
faster than this speed is not possible, except when kick-down is applied [7]. While there is
considerable public support for ISA, an implementation strategy is needed to speed up the
process of implementation of ISA in vehicles. Further harmonisation activities are needed at
international level. Uptake has been slow due to missing road data or incomplete periodic
upgrade of speed limits displayed. In some countries it is very difficult to gather all data from
the different administration layers (towns, regions, departments, national roads etc). The
system is applied in Sweden to all state-owned vehicles, and provided as an option in the
higher end of the market. It is operational on a few parts of the road network, in some other
EU countries [6].
− Collision Avoidance Systems (CAS)
Research on collision warning and collision avoidance systems is taking place in Japan, the
United States and in the European Union through the eSafety programme.
DAS applications have been first introduced, and will continue to do so, in the luxury segment
before trickling down to the mass market. Table 13 shows the penetration rate for some DAS
applications in the European premium car fleet [8].
Deliverable D6.1 38
Table 13 Overview of DAS penetration rates in premium cars in Europe
DAS Application
Penetration rate in
premium cars in
Europe by 2008
Comments
Anti Blocking System (ABS) 100% Already standard today
Adaptive Cruise Control (ACC) 50-60% Prerequisite for further assistance
systems (e.g. Stop & Go)
Lane Departure Warning (LDW) 10-20% Steering interference also possible in
expansion stage
Lane Change Assistant (LCA) 5-10% Requires powerful sensors (24 GHz)
Table 14 shows the penetration rates for some DAS applications that have been assessed by
the eIMPACT Project for 2010 and 2020 [9].
Table 14 Penetration rates of DAS applications in Europe according to eIMPACT
DAS Application Penetration Rate in Europe
2010 2020
Electronic Stability Control (ESC) 25-30% 58-75%
eCall 0.1-0.3% 35-50%
Adaptive Cruise Control (ACC) 0.01-0.1% 3.6-11%
Lane Departure Warning (LDW) 0.9-2.2% 6-18%
Lane Change Assistant (LCA) 0.2-0.7% 3.3-9%
Intelligent Speed Adaptation (ISA) 2-3% 25-39%
References
1. Transportation Research Board (TRB) (2000). Highway Capacity Manual Washington,
DC, USA: National Research Council, 2000.
2. Featherstone C., Lowson M. (2004). Viability and Benefits of Platooning in Automated
Transport Systems. CyberCars Project.
3. European Commission (2010). Mobility and Transport, TEN-T/Transport infrastructure,
TEN-T Components, Road. Retrieved September 12, 2010 from:
http://ec.europa.eu/transport/infrastructure/networks_eu/road_en.htm
Deliverable D6.1 39
4. European Commission (2010). Mobility and Transport, TEN-T/Transport infrastructure,
TEN-T Components, Length of TEN-T roads per country and link type. Retrieved
September 12, 2010 from:
http://ec.europa.eu/transport/infrastructure/networks_eu/doc/rail_tab1_country_len
gth_ten-t_rail.pdf
5. SafetyNet (2009). eSafety-web text. Retrieved 16/10/2009. Available on:
http://ec.europa.eu/transport/road_safety/specialist/knowledge/pdf/esafety.pdf
6. Commission of the European Communities (2008). Action Plan for the Deployment of
Intelligent Transport Systems in Europe and the Proposal for a Directive of the
European Parliament and of the Council. SEC(2008) 3083, Brussels.
7. ADVISORS Project (2002). Inventory of ADAS and User Needs. Deliverable D1.2 v12.3.
8. http://www.oliverwyman.com/ow/pdf_files/20070709_AutoSafetyTechnology_c.pdf
9. eIMPACT (2008). Socio-economic Impact Assessment of Stand-alone and Co-operative
Intelligent Vehicle Safety Systems (IVSS) in Europe. Impact Assessment of Intelligent
Vehicle Safety Systems. Deliverable D4.
Deliverable D6.1 40
Annex B
B1: Questionnaires
Within WP5, a questionnaire was designed and given to experts. The objective was to obtain
the opinion of experts on several issues regarding Intelligent Transport Systems such as their
impact on capacity, energy use, GHG emissions as well as on social and economical issues.
The questionnaire is presented below.
Questionnaire
for EC-FP7 Project:
Technology Options and Strategies Toward Climate-Friendly Transport (TOSCA)
The purpose of this questionnaire is to quantify the uncertainty associated with techno-
economic characteristics of future low-greenhouse gas emission transportation technologies
and fuels, which are being evaluated in the EC FP7-funded TOSCA project. These estimates will
then be used in a scenario analysis for reducing transport sector greenhouse gas emissions.
The questionnaire examines the Intelligent Transport Systems (ITS) that could be implemented
in the European road infrastructure and vehicle fleet over the next 40 years for passenger
traffic.
Although we would be grateful if you indicated your name, your response will be treated
confidentially and will not be revealed to outside the TOSCA project. For information about
the TOSCA project, please visit our website http://www.toscaproject.org/ or contact:
Voula Psaraki
Assistant Professor
National Technical University of Athens
E-mail address: [email protected]
Deliverable D6.1 41
Automated Highway System (AHS)-Platoons
The Automated Highway System (AHS) is a concept where vehicles organize themselves into platoons
and are linked together in communication networks, which allow the continuous exchange of
information about speed, acceleration, braking and obstacles. The platoons operate in dedicated lanes.
The platoon vehicles are equipped with longitudinal and lateral control system, advanced steering and
cruise control and systems needed for platoon operations.
1. When do you think the AHS is expected to enter the European market (market readiness)?
.........................................................................................................................................................
2. Do you think that platoons will improve the road capacity? If yes, how much in comparison with
the current capacity of a highway (1800-2400 vehicles/hour/lane)?
........................................................................................................................................................
3. Do you think that platoons will contribute to CO2 emission reduction? If yes, how
much?..............................................................................................................................................
4. How much do you think that the additional retail price of the equipment in the passenger
vehicle will be to ensure platoon operations?……………………………………………………………………………..
5. How much do you think that the additional operating costs of the passenger vehicle will be
when operating in a platoon?...........................................................................................................
6. Do you think that passenger cars will pay tolls in order to operate in
platoons?..........................................................................................................................................
7. In your opinion, what are the R&D requirements for platoon development in Europe? Please
choose from: insignificant, significant (company level), substantial (EU-wide
program)……………….........................................................................................................................
8. Do you believe that there are issues of social acceptability such as the following? Please rate
from -- (unacceptable adverse effect) to ++ (significant benefits); 0 being “comparable to
existing systems”.
a. Social equity implications…………………….....................................................................................
b. Generation of jobs within EC……………………………………………………………………..............................
c. Passengers Safety…………………………………………………………………………………………….......................
d. Privacy issues …………………………………………………………………………………….....................................
e. Driving Comfort and Acceptance……………………………….............................................................
f. Liability issues………….………............……......…....………............………............……….........................
g. Other user acceptability (driverless vehicles)…………………….……............…................... ............
10. Do you have any other comments on the issues related to AHS?
…………………………………………………………………………………………………………………………………………............
Deliverable D6.1 42
B2: Questionnaire Results
The next diagrams show the answers of the experts on issues related to the AHS. On the
whole, 19 experts participated in the survey either by direct interview or by mail. However, as
experts did not address all issues, the number of responses varies from one question to
another. Experts include participants in EU-funded projects, academics, experienced
transportation engineers etc.
Question 1: When do you think the AHS is expected to enter the European market (market
readiness)?
4
9
2 3
0
2
4
6
8
10
Frequ
ency
.
2025 2030-2040 2050 maybe never
Market Readiness of AHS
N=18
Question 2: Do you think that platoons will improve the road capacity? If yes, how much in
comparison with the current capacity of a highway (1800-2400 vehicles/hour/lane)?
3
5
4
3
0
1
2
3
4
5
Fre
quency
.
10-20 % 21-30% 50-80% 300%
Capacity Improvement
Road Capacity Improvement by AHS
N=15
Deliverable D6.1 43
Question 3: Do you think that platoons will contribute to fuel consumption reduction? If yes,
how much?
7
8
10
1
2
3
4
5
6
7
8
Fre
quency
.
5-10% 15-25% No effect
CO2 Emissions Reduction
CO2 Emissions Reduction by AHS
N=16
Question 4: How much do you think that the additional retail price of the equipment in the
passenger vehicle will be to ensure platoon operations?
1 1
13
2
0
2
4
6
8
10
12
14
Fre
quency
.
0 € 500 € 1000-2000 € 4000-5000 €
Additional Retail Price
Additional Retail Price of the AHS-equipped car
N=17
Question 5: How much do you think that the additional operating costs of the passenger
vehicle will be when operating in a platoon?
5
8
1
0
1
2
3
4
5
6
7
8
Fre
quency
.
0 €/year 50-200 €/year 500 €/year
Additional Operating Costs
Additional Operating Costs of the AHS-equipped car
N=14
Deliverable D6.1 44
Question 6: In your opinion, what are the R&D requirements for platoon development in
Europe? Please choose from: insignificant, significant (company level), substantial (EU-wide program).
2
14
0
2
4
6
8
10
12
14
Fre
quency
.
Insignificant Significant Substantial
R&D Requirements for AHS
N=16
Question 7: Do you believe that there are issues of social acceptability such as the following?
Please rate from -- (unacceptable adverse effect) to ++ (significant benefits); 0 being “comparable to
existing systems”.
1
7
6
1 1
0
1
2
3
4
5
6
7
Fre
qu
ency
.
Unac
cep
tab
le
eff
ect
(- -)
Ad
vers
e
eff
ect
(-)
Co
mpar
able
to r
efe
ren
ce
(0)
Signif
ican
t
be
ne
fits
(+)
Ve
ry
signif
ican
t
be
nefi
ts (
++)
Social Equity Implications for AHS
N=16
5
7
4
0
1
2
3
4
5
6
7
Fre
quency
.
Unac
cep
tab
le
eff
ect
(- -)
Ad
vers
e
eff
ect
(-)
Co
mp
arab
le
to r
efe
ren
ce
(0)
Sign
ific
ant
be
ne
fits
(+
)
Ve
ry
sign
ific
ant
be
ne
fits
(+
+)
Generation of jobs within EC by AHS
N=16
3
7
6
0
1
2
3
4
5
6
7
Freq
uen
cy
.
Unacc
epta
ble
effect
(- -)
Advers
e
effe
ct (-)
Com
par
able
to r
efe
rence
(0)
Signific
ant
be
ne
fits
(+)
Ve
ry
signif
ican
t
ben
efi
ts (+
+)
Passengers Safety by AHS
N=16
3
4
9
0
1
2
3
4
5
6
7
8
9
Fre
quency
.
Un
acce
pta
ble
effe
ct (-
-)
Adve
rse
effe
ct (-)
Co
mp
arab
le
to r
efe
ren
ce
(0)
Sign
ific
ant
ben
efits
(+
)
Ve
ry
sign
ific
ant
ben
efits
(++
)
Privacy Issues for AHS
N=16
Deliverable D6.1 45
12
10
3
0
1
2
3
4
5
6
7
8
9
10
Freq
uency
.
Unacce
pta
ble
eff
ect
(- -)
Advers
e
effe
ct (
-)
Co
mp
arable
to r
efe
ren
ce
(0)
Sig
nific
ant
ben
efi
ts (
+)
Very
sign
ific
ant
be
ne
fits
(+
+)
Driving Comfort and Acceptance for AHS
N=16