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Vehicle and Road Automation 66.2 Integrated Vehicle-Based Safety Systems (IVBSS) 1175

Serial

RDCW system CAN bus (500K)

Nissan vehicle ECU CAN bus (500K)

Radar CAN bus 1 (500K)

Radar CAN bus 2 (500K)

Navigation

module

Curve speed

warning (CSW)

module

Lateral drift

warning (LDW)

module

Driver

display

Data acquisition

system (DAS)

module

Situational

awareness

module (SAM)

Driver vehicle

interface (DVI)

module

Forward

radar

Cell

phone

detect

GPS

antenna

ADAS

map

Com-

ment

Cellular

antenna

Front

left

Front

right

Side

radar

Left

side

Right

side

Steering

wheel

angle

Inertial

sensors

Driver

face

camera

LSW

sensi-

tivity

Gyro

CSW

sensi-

tivity

CCD

LDW

camera

OEM

radio

DVI

DAS

forward

camera

Audio

mic

USB

Serial

Panel link

Warning enabled

Audio matrix

module

OEM

amplifier

Haptic

controller

Fig. 66.6 RDCW system architecture (after [66.15]) (PB – push button)

Understanding driver behavior is also important

in minimizing nuisance or false alerts, which not

only cause stress and irritation to the driver but may

also cause driver distraction. For instance intentional

lane changes involve particular actions by the driver

which may resemble actions taken when an accident

is imminent. Therefore precisely differentiating driver

behaviors is necessary.

How is human factors research conducted? Vehi-

cle simulators used by NHTSA, car manufacturers,

research centers, and driver training centers are valu-

able in the initial research and development (R&D) of

vehicle technologies. In the continuum of assessment

methodologies, simulators offer the greatest range of

possible scenarios and driving conditions within a safe

setting and are at the lowest end in terms of noncap-

ital resource expenditures. Modular design simulators

have interchangeable cabs which can be conﬁgured for

different vehicle models. Multistation driving simulator

allows one controller/instructor to control multiple driv-

ing stations, enabling simultaneous multiple simulation

runs. Recently Toyota introduced its new simulator that

imitates speed, acceleration, deceleration, comfort con-

ditions, and conditions at intersections. The simulator

is comprised of eight high-precision projectors within

a large dome which has a turntable, tilt mechanism, and

vibration equipment. The dome is on a track that allows

forward, backward, and side-to-side movement. Jaguar

and Land Rover’s engineering facility in the UK also in-

troduced a state-of-the-art simulator that relies more on

virtual reality to create three-dimensional (3-D) vehicle

exteriors and interiors [66.16]. Less sophisticated sim-

ulators might consist of a steering wheel connected to

a monitor displaying roadway images.

Part G 66.2

1176 Part G Infrastructure and Service Automation

A proving ground is the next step in the develop-

ment and testing process. Before systems are tested on

the road, it is prudent to test them in a more controlled

environment. Most automakers have their own proving

ground or use an independent facility. These facilities,

like simulators, range in size and number of features.

Some facilities may have 50 miles of roadway of vary-

ing types. After the system has undergone additional

modiﬁcations and ﬁne-tuning, realistic driving scenar-

ios should be evaluated.

Field testing on actual roadways is resource in-

tensive because multiple vehicles need to be ﬁtted

out with the technologies and then test subjects re-

cruited to drive the vehicles for a certain number of

weeks. Usually baseline data without the use of assis-

tance or warning systems to reveal a driver’s natural

driving behavior are gathered initially and then com-

pared against their behavior after the systems are turned

on.

To be able to generalize test results in ﬁeld tests,

a sufﬁcient number of test subjects should be se-

lected from a diverse population in terms of age,

gender, and driving experience; testing should be

conducted under a variety of weather and trafﬁc con-

ditions, different roadway geometry, and freeway and

local roadways in urban, suburban, and rural settings;

and, optimally, it should be performed in real-world

settings.

66.3 Vehicle Infrastructure Integration (VII)

In order to connect drivers, roads, and vehicles, the

USVehicle Infrastructure Integration or VII has been

established. VII isenvisioned to be a national communi-

cations infrastructure and will enable vehicle-to-vehicle

and vehicle-to-roadside communications. Thededicated

short-range communications (DSRC)5.9 GHz system,

a reliable short-range wireless technology, has been

designated for transportation and public safety applica-

tions. What will be required will be the development of

national standards for interoperability, close collabora-

tion among USDOT, state DOTs, and vehicle makers,

the development of key technologies, and the resolution

of political, institutional, legal, economic, and social

issues connected with the infrastructure.

The four phases of the VII initiative are [66.17]:

1. Case and application development planning

2. Proof-of-concept testing

3. Prototype development, testing, and analysis.

Human factor issues will be addressed, real-world

applications reﬁned and expanded, and institutional

challenges considered, such as citizen anonymity,

privacy, and security.

4. The ﬁnal step is deployment readiness and support,

including identifying the resources, policies, and

governance necessary to move forward.

Phase 1 has been completed, and phase 2 testing

is now underway, with California and Michigan hav-

ing the most VII test-bed infrastructure. Also, a large

demonstration of VII is planned in New York City in

November of 2008 at the 2008 World Congress and ITS

America 2008 annual meeting.

66.3.1 VII Beneﬁts

VII safety beneﬁts are brought about by collision

warning systems on highways and local arterials, inter-

section collision avoidance technology, trafﬁc violation

warnings, and roadway curve warning; for example

a broken-down vehicle on an interstate could send wire-

less messages to an approaching semitrailer truck, and

on to the cars behind it. According to Bob Lange, exec-

utive director of vehicle structure and safety integration

at General Motors Corp, it could even apply the car’s

brakes automatically if the driverbehind thetruck isdis-

tracted. If there is a potential conﬂict at an intersection,

the roadside infrastructure could transmit appropriate

messages to the vehicles approaching the intersection,

and the vehicles would then alert the drivers. Similarly,

warnings would be issued if another car is in a vehi-

cle’s blind spot, or if a lane change is required due to

construction work.

Other beneﬁts enhance the mobility of drivers and

their access to desired destinations. In-vehicle naviga-

tion systems direct drivers by voice to their destinations

and provide convenient real-time turn-by-turn direc-

tions. Some systems have voice recognition capability

so that the driver need not manually input the des-

tination, and other systems assist drivers optimize

their trips and can incorporate personal preferences

such as avoiding highways. In-vehicle information sys-

tems assist drivers ﬁnd destinations such as hotels,

restaurants, and gas stations, and may even have real-

time information comparing gas prices at different

stations. These systems can also help drivers avoid con-

Part G 66.3

Vehicle and Road Automation 66.4 Conclusion and Emerging Trends 1177

gested roads by providing real-time incident and delay

information.

Vehicles, acting as information probes, would col-

lect and transmit data about trafﬁc and road conditions

anonymously to the roadside infrastructure, which

would then process the information and send it out to

the vehicles on the road; users of the data could be

charged a subscription fee, making VII a viable busi-

ness opportunity for the private sector. This information

can also be used by transportation planners and traf-

ﬁc management center (TMC) operators for corridor

management tasks.

66.3.2 VII Risks

Historically the majority of potential technology users

will only adopt once the very early users, who are will-

ing to experiment, try and approve a new technology;

cellphones, for instance, took 20years before achiev-

ing widespread consumer acceptance [66.18]. The VII

infrastructure will take a large amount of resources to

deploy, about US$6–8billion, which will likely require

Congressional approval or a combination of govern-

ment and private-sector involvement.

The primary risks for VII are thefollowingtechnical

and market risks.

Technical risks include: the need to integrate mul-

tiple technologies; ensuring connectivity within the

DSRC network, and a determination of how to handle

the data generated by the system; the development of

a technology that will be able to perform real-time ana-

lysis of data from many different input sources. There

is also a question over whether the technologies will be

effective if only a portion of users adopt them. Also,

how the entire country will be mapped, and how VII

can be ﬂexibly applied to local needs, will need to be

determined.

Marketrisks includethe identiﬁcationof fundingfor

the system, user acceptance and willingness-to-pay is-

sues, and difﬁculty in accurately assessing system value

until after the system has been implemented. From

the automakers’ perspective, the introduction of new

features and innovations is always risky, particularly

a safety feature which could increase the likelihood of

lawsuits. Therefore the business case for automakers to

incorporate these systems into their vehicles must be

especially strong.

Because of the uncertainty with regards to which

technologies in which form will receive the greatest

user acceptance, an open architecture would be rec-

ommended. Also, the development and use of industry

standards will be important. Industry standards such as

local interconnect network and media-oriented systems

transport are being created for communication buses to

link electronic control units, subsystems, and sensors.

Automotive open system architecture (AUTOSAR),

a partnership of auto manufacturers, suppliers, and

tool developers, is attempting to become the de facto

standard-setting organization for the auto industry on

a global basis. AUTOSAR seeks to minimize barriers

between functional domains and networks indepen-

dently from the hardware and to manage the increasing

complexity of the technology. AUTOSAR is an open

and standardized global automotive software architec-

ture with the following motto: Cooperate on standards,

compete on implementation [66.19].

66.4 Conclusion and Emerging Trends

While fully automated driverless vehicles and ﬂying

cars are many years away, ITS technologies have been

increasing the capabilities, intelligence, and safety of

the automobile. An early generation of such vehicles

can be seen in Figs. 66.7 and 66.8. The percentage

of total vehicle value accounted for by electronics and

software is currently 15% for compact cars, 28% for

luxury cars, and 47% for hybrid cars, and is expected

to rise yearly. The continuing need for mobility and

Fig. 66.7 Defense Advanced Research Projects Agency

(DARPA) car at the Intelligent Vehicle Conference 2008,

Eindhoven

Part G 66.4

1178 Part G Infrastructure and Service Automation

safety enhancements will ensure the growth of the value

of electronics and software; the increase is estimated

to be about 150%, from US$187billion in 2002 to

US$464 billion in 2015 [66.20].

Much progress has already been made in deploy-

ing individual ITS technologies, and many mobility

and safety beneﬁts are already being experienced.

The National ITS Architecture has been the unifying

framework allowing the disparate transportation tech-

nologies to work together; and vehicle infrastructure

integration will be the engine by which vehicle-to-

vehicle and vehicle-to-roadside communications will

take place. Once the Integrated Vehicle-Based Safety

Systems initiative is completed and the safety-oriented

ITS technologies are implemented, even more safety

beneﬁts are expected to be realized.

Fig. 66.8 Carnegie Mellon entry to the DARPA 2007

Urban Challenge competition, Boss, Tartan racing robot

(courtesy of Tartan Racing, Carnegie Mellon University)

66.5 Further Reading

•

J. Baber, J. Kolodko, T. Noel, M. Parent, L. Vlacic:

Cooperative autonomous driving: intelligent vehi-

cles sharing city roads, IEEE Robot. Autom. Mag.

12(1), 44–9 (2005)

•

S. Bahler, J. Kranig, E. Minge: Field test of non-

intrusive trafﬁc detection technologies. In: Trans-

portation Research Record 1643, ed. by Transporta-

tion Research Board (National Research Council,

Washington 1998) pp.161–170

•

D. Braid, A. Broggi, G. Schmeidel: The TerraMax

autonomous vehicle, J. Field Robot. 23(9), 693–708

(2006)

•

J.L. Campbell, C.M. Richard, J.L. Brown, M. Mc-

Callum: Crash Warning System Interfaces: Human

Factors Insights and Lessons Learned (Battelle

Center for Human Performance and Safety, Seattle

2006)

•

K. Catchpole, J.D. McKeown, D.J. Withington:

Localizable auditory warning pulses, Ergonomics

47(7), 748–771 (2004)

•

US Department of Transport: Crash Avoidance

Metrics Partnership (CAMP): Enhanced digital

mapping project ﬁnal report; US Department of

Transport Tech. Rep. (2004)

•

US Department of Transport: Crash AvoidanceMet-

rics Partnership (CAMP): Vehicle safety communi-

cations project task 3 ﬁnal report; US Department of

Transport Tech. Rep. (2005)

•

A. Houser, J. Pierowicz, D. Fuglewicz: Concept

of Operations and Voluntary Operational Require-

ments forLane Departure Warning Systems (LDWS)

On-Board Commercial Motor Vehicles, FMCSA-

MCRR-05-005 DTMC75-03-F-0087 (Federal Mo-

tor Carrier Safety Administration, Washington

2005)

•

A. Houser, J. Pierowicz, R. McClellan: Concept

of Operations and Voluntary Operational Require-

ments for Forward Collision Warning Systems

(CWS) and Adaptive Cruise Control (ACC) Systems

On-Board Commercial Motor Vehicles, FMCSA-

MCRR-5-007 DTMC75-03-F-0087 (Federal Motor

Carrier Safety Administration, Washington 2005)

•

International Organization for Standardization: In-

telligent transportation systems – forward vehicle

collision warning systems – performance require-

ments and test procedures, Draft International

Standard ISO/DIS 15623 (2002)

•

International Organization for Standardization: In-

telligent transportation systems – lane depar-

ture warning systems – performance requirements

and test procedures, Draft International Standard

ISO/DIS 17361 (2004)

•

International Organization for Standardization: In-

telligent transportation systems – lane change

decision aid systems – performance requirements

and test procedures, Draft International Standard

ISO/DIS 17387 (2006)

•

ITS Public Safety:

http://www.its.dot.gov/pubsafety/index.htm(last ac-

cessed on May 5, 2009)

Part G 66.5

Vehicle and Road Automation 66.5 Further Reading 1179

•

IVBSS Project Team: Preliminary Functional Re-

quirements for the Integrated Vehicle-Based Safety

System (IVBSS) –Heavy-Truck Platform (University

of Michigan Transportation Research Institute, Ann

Arbor 2007)

•

IVBSS Project Team: Preliminary Performance

Guidelines for a PrototypeIntegratedVehicle-Based

Safety System (IVBSS) – Light-Vehicle Platform

(University of Michigan Transportation Research

Institute, Ann Arbor 2007)

•

R.J. Kiefer, D. LeBlanc, M. Palmer, J. Salinger,

R. Deering, M. Shulman: Development and Val-

idation of Functional Deﬁnitions and Evaluation

of Procedures for Collision Warning/Avoidance

Systems, NHTSA Rep. HS 808 964 (National

Highway Trafﬁc Safety Administration, Washing-

ton 1999)

•

R.J. Kiefer, D. LeBlanc, M. Palmer, J. Salinger,

R. Deering, M. Shulman: Forward Collision Warn-

ing Systems: Development and Validation of Func-

tional Deﬁnitions and Evaluation Procedures for

Collision Warning/Avoidance Systems,Rep.DOT-

HS-808-964 (Department of Transportation, Wash-

ington 1999)

•

R.J. Kiefer, M.T. Cassar, C.A. Flannagan, D.J.

LeBlanc, M.D. Palmer, R.K. Deering, M.A. Shul-

man: Forward Collision Warning Requirements

Project: Reﬁning the CAMP Crash Alert Timing

Approach by Examining “Last-Second” Braking

and Lane Change Maneuvers Under Various Kine-

matic Conditions (2003)

•

D. LeBlanc: Road DepartureCrashWarningSystem

Field Operational Test – Methodology and Results,

Rep. UMTRI-2006-9-1 (University of Michigan

Transportation Research Institute, Ann Arbor 2006)

•

R. Madhavan, E.R. Messina, J.-S. Albus: Intelli-

gent Vehicle Systems: A 4D/RCS Approach (Nova

Science, Hauppauge 2007)

•

W.G. Najm, J.D. Smith: Development of Crash Im-

minent Test Scenarios for Integrated Vehicle-Based

Safety Systems (IVBSS), Department of Transport

VNTSC-NHTSA-07-01 DOTHS 810757 (National

Highway Trafﬁc Safety Administration, Washing-

ton 2007)

•

Y. Nakanishi, J. Western: Ensuring the security of

transportation facilities: An evaluation of advanced

vehicle-identiﬁcation technologies, Transportation

Research Board 84th Annual Meeting (2005), CD-

ROM

•

NHTSA: Preliminary Assessment of Crash Avoid-

ance Systems Beneﬁts (National Highway Trafﬁc

Safety Administration, Washington 1996), Version

II, Chap. 3

•

D. Pomerleau, J. Everson: Run-Off-Road Collision

Avoidance Using IVHS Countermeasures – Final

Report, NHTSA Rep. DOT HS 809 170 (National

Highway Trafﬁc Safety Administration, Washing-

ton 1999)

•

J.R. Sayer, J. Devonshire, C.A. Flannagan: Ef-

fects of Secondary Tasks on Driving Performance,

Rep. UMTRI-2005-29 (The University of Michigan

Transportation Research Institute, Ann Arbor 2005)

•

A. Stewart: The development of driverless au-

tonomous vehicles, EngineerIT Nov/Dec, 50–52

(2006)

•

S. Szabo, R. Norcross: Recommendations for objec-

tive test proceduresfor road departurecrash warning

systems, Memorandum to NHTSA (2003)

•

S. Talmadge, R. Chu, C. Eberhard, K. Jordan,

P. M offa: Development of Performance Speciﬁca-

tions for Collision Avoidance Systems for Lane

Change Crashes, NHTSA Tech. Rep. (National

Highway Trafﬁc Safety Administration, Washing-

ton 2001)

•

A.K. Tan, N.D. Lerner: Multiple Attribute Evalu-

ation of Auditory Warning Signals for In-Vehicle

Crash Avoidance Warning Systems (National High-

way Trafﬁc Safety Administration, Washington

1995)

•

C.E. Thorpe: Side Collision Warning System

(SCWS) Performance Speciﬁcations for a Transit

Bus, Federal Transit Administration under Pen-

nDOT 62N111 Project TA (Federal Transit Admin-

istration, Washington 2002)

•

S.M. Turner, W.L. Eisele, R.J. Benz, D.J. Hold-

ener: Travel Time Data Collection Handbook,

USDOT/FHWA-PL-98-035 (Federal Highway Ad-

ministration, Washington 1998)

•

University of Michigan Transportation Research In-

stitute: Integrated Vehicle-Based Safety Systems

(IVBSS), First Annual Report (US Department of

Transport, 2007)

•

C. Urmson, W. Whittaker: Self-driving cars and the

urban challenge, IEEE Intell. Syst. 23(2), 66–68

(2008)

•

US Department of Transportation: IVBSS Program

Plan,

http://www.its.dot.gov/ivbss/ivbss_workplan.htm

(2005), retrieved June 18, 2007

•

US Department of Transportation: Intelligent vehi-

cle initiative ﬁnal report, Tech. Rep. FHWA-JPO-

05-057 (2005)

Part G 66.5

1180 Part G Infrastructure and Service Automation

•

US Department of Transport:

http://www.its.dot.gov/vii/index.htm (last accessed

May 5, 2009)

•

VII Coalition Information:

http://www.vehicle-infrastructure.org (last accessed

May 5, 2009)

References

66.1 Trafﬁc Safety Facts 2006, DOT HS 810 809 (NHTSA,

2006), updated 2008

66.2 Annual study shows trafﬁc congestion worsening

in cities large and small, Texas Transportation In-

stitute Press Release Sept. 18, 2007

66.3 US Department of Transport ITS Beneﬁts Database:

http://www.beneﬁtcost.its.dot.gov (2009)

66.4 National ITS Architecture:

http://www.iteris.com/itsarch/ (2009)

66.5 Y.J. Nakanishi: Introduction to ITS. In: Economic

Impacts of Intelligent Transportation Systems: In-

novations and Case Studies, ed. by E. Bekiaris,

Y. Nakanishi (Elsevier, Oxford 2004) pp. 3–16,

Chap. 1

66.6 J. Njord, J. Peters, M. Freitas, B. Warner, K.C. Allred,

R. Bertini, R. Bryant, R. Callan, M. Knopp,

L. Knowlton, C. Lopez, T. Warne: Safety Appli-

cations of Intelligent Transportation Systems in

Europe and Japan (US Department of Transport,

2006)

66.7 European Commission: http://www.prevent-ip.org/

(2008)

66.8 IVBSS Project Team: Preliminary Functional Re-

quirements for the Integrated Vehicle-Based Safety

System (IVBSS) – Light-Vehicle Platform (University

of Michigan Transportation Research Institute, Ann

Arbor 2007)

66.9 R.J. Kiefer, M.T. Cassar, C.A. Flannagan, D.J. LeBlanc,

M.D. Palmer, R.K. Deering, M.A. Shulman: Forward

Collision Warning Requirements Project Final Re-

port – Task 1, NHTSA Rep. HS 809 574 (National

Highway Trafﬁc Safety Administration, Washington

2003)

66.10 University of Michigan Transportation Research In-

stitute and General Motors: Automotive Collision

Avoidance System (ACAS) Field Operational Test –

Methodology and Results, NHTSA Rep. HS 809 901

(National Highway Trafﬁc Safety Administration,

Washington 2005)

66.11 General Motors, Delphi Electronic Systems: Au-

tomotive Collision Avoidance System (ACAS) Field

Operational Test – Final Program Report,NHTSA

Tech. Rep. DOT HS 809 866 (National Highway Traf-

ﬁc Safety Administration, Washington 2005)

66.12 General Motors: Automotive Collision Avoidance

System Field Operational Test: Final Program Re-

port, US Department of Transport Rep. DOT HS

809 886 (US Department of Transport, Washington

2005)

66.13 D. LeBlanc, J. Sayer, C. Winkler, S. Bogard, J. De-

vonshire, M. Mefford, M.L. Hagan, Z. Bareket,

R. Goodsell, T. Gordon: Road Departure Crash

Warning System (RDCW) Field Operational Test –

Final Report (National Highway Trafﬁc Safety Ad-

ministration, Washington 2006)

66.14 L.R. Shelton: Statement before the subcommit-

tee on highways and transit, Committee on

Transportation and Iinfrastructure, US House of

Representatives (2001), http://www.nhtsa.dot.gov/

nhtsa/announce/testimony/distractiontestimony.

html

66.15 University of Michigan Transportation Research In-

stitute, Visteon, AssistWare Technologies Inc.: Road

Departure Crash Warning Field Operational Test:

Interim Report on Road Departure Crash Warning

Subsystems (US Department of Transport, Washing-

ton 2003)

66.16 J. Challen: Reality bytes, Automot. Test. Technol.

Int. March, 48–51 (2008)

66.17 IVI Final Report: Saving Lives Through Advanced

Vehicle Safety Technology (2005)

66.18 Lessons Learned and their Impact on VII Applica-

tions in VII: An Alternative Perspective (Northeast-

ern University, 2007)

66.19 Autosar: http://www.autosar.org/ (Munich 2008)

66.20 J. Gunn: Speculate to communicate: The surge of

in-car electronics, Trafﬁc Technol. Int. Feb/Mar

(2008)

Part G 66

1181

Air Transporta

67. Air Transportation System Automation

Satish C. Mohleji, Dean F. Lamiano, Sebastian V. Massimini

The air transportation system infrastructure is

comprised of communication, navigation, surveil-

lance, and air trafﬁc management systems, and

is known as the National Airspace System. This

chapter describes the current very high-frequency

and high-frequency modes of communication,

very high-frequency omnidirectional range, dis-

tance measuring equipment, and instrument

landing systems for navigation/guidance to the

aircraft, and primary, as well as secondary radars,

for surveillance. The two primary functions of

the ground-based air trafﬁc management sys-

tem, viz. trafﬁc ﬂow management for strategic

air trafﬁc planning and air trafﬁc control for

safe movement of aircraft, are discussed in

detail. This chapter also addresses the limited

role of automation in both the aircraft cockpit

and the ground-based air trafﬁc management

system.

67.1 Current NAS CNS/ATM

Systems Infrastructure ..........................1183

67.1.1 Air/Ground Communications

Systems and Functions................ 1184

67.1.2 Navigation and Guidance Systems 1186

67.1.3 Modes of Navigation................... 1189

67.1.4 ATC Surveillance Systems

and Aircraft Tracking .................. 1190

67.1.5 Aircraft Tracking ......................... 1192

67.1.6 Air Trafﬁc Management Functions 1192

67.2 Functional Role

of Automation in Aircraft

for Flight Safety and Efﬁciency ..............1194

67.3 Functional Role of Automation

in the Ground System

for Flight Safety and Efﬁciency ..............1195

67.3.1 Minimum Safe Altitude Warning... 1195

67.3.2 Conﬂict Alert.............................. 1195

67.3.3 User Request Evaluation Tool ....... 1196

67.3.4 Trafﬁc Management Advisor ........ 1196

67.4 CNS/ATM Functional Limitations

with Impact on Operational Performance

Measures .............................................1196

67.4.1 Current Separation Minima

for Controlled IFR Aircraft ............ 1197

67.4.2 Flight Safety Assessment Metrics .. 1199

67.4.3 Determination of Airport Capacity 1199

67.4.4 Aircraft Delays

to Measure Operational Efﬁciency 1202

67.4.5 Measuring Controller Workload .... 1202

67.5 Future Air Transportation

System Requirements

and Functional Automation...................1203

67.5.1 Automation Approach to Meet

Future Air Transportation System

Requirements ............................ 1204

67.5.2 Development of Automated

Functional Capabilities ............... 1204

67.6 Summary .............................................1211

References ..................................................1212

The US civil air transportation system today handles

over 700million passengers and 40 billion revenue ton

miles of cargo annually that require about 23million

aircraft operations. Each aircraft operation represents

a ﬂight from take off to touch down between its

departure and destination airports. This number is fore-

casted to reach a level of 35million operations by the

year 2025. Although the air transportation system has

recorded a very high level of safety over the years, the

continued growth in demand for air trafﬁc services has

created operational inefﬁciencies, which result in signif-

icant ﬂight delays and lost revenue for the users. Today,

a majority of decisions in the aircraft and in the ground

control system are manual. The human decision making

Part G 67

1182 Part G Infrastructure and Service Automation

is intensivefor the pilots andcontrollers interms oftheir

workloads, and will become unmanageable as trafﬁc

levels grow. In order to deal with the trafﬁc growth, the

future air transportation system will require increased

automation of air/ground functions to enhance safety,

capacity, and ﬂight efﬁciency while reducing delays and

workload.

The performance of the air transportation system

is measured by four key metrics relating to safety, ca-

pacity, ﬂight efﬁciency/delays, and workload. Because

aircraft safety is of the utmost importance, the US Gov-

ernment has established regulations that require the

aircraft to maintaina minimum separationdistance from

other aircraft. These separation minima vary with the

phases of ﬂight. Explanations of the separation min-

ima and their inﬂuence, as well as limitations on ﬂight

operations, are provided. How the use of automation

could in the future overcome these limitations is also

discussed.

Effortsare underway to deﬁne concepts for the next-

generation (NextGen) air transportation system in the

USA, and the Single European Sky air trafﬁc man-

agement (AT M) research project in Europe. The Joint

Planning and Development Ofﬁce, mandated by the US

Congress, is charted to develop NextGen concepts for

the years 2025 and beyond. The goals and requirements

of the NextGen system are presented. Also discussed

is the ongoing research and development of new satel-

lite technologies, including data link and satellite-based

navigation and surveillance, as well as the needed au-

tomation of ground-based decision support functions.

It is expected that enhanced automation in the aircraft

and in the ground system will provide safe and efﬁcient

services to a signiﬁcantly higher number of aircraft op-

erations in the future.

The civil air transportation system is an essential

component of the global economy. It is required for

timely movement of people and cargo. Until now, air

travel has been the safest mode of transportation per

passenger mile. Because of the cost, air transportation

has not been the primary mode of transportation for

long-distance travel in most countries, even though it

has been widely used in the USA. However, the demand

for air travel around the world is now increasingat a fast

pace to meet multinational commerce needs. For glob-

ally harmonized aircraft operations, the International

Civil Aviation Organization (ICAO), in collaboration

with National Civil Aviation Authorities (CAA), has

established standards and operating procedures to be

followed by all aircraft operators and air trafﬁc service

providers.

In the US, the National Airspace System (NAS)is

a complex system of human-centric systems providing

communication, navigation, and surveillance (CNS),

and air trafﬁc management (AT M) services to air-

craft ﬂying passengers and cargo. During 2006, over

700million passengers used NAS, and the demand for

air travel is expected to increase beyond one billion pas-

sengers by the year 2015. The annual cargo revenue ton

miles exceeded 40billion in 2006 [67.1], and is contin-

uing to grow with the signiﬁcant demand for goods and

services.

In order to meet the challenges of greater demand

for air travel, it is imperative to have an air trans-

portation system that not only maintains or enhances

safety, but also provides efﬁcient ﬂight operations.

The current aircraft operations and the air trafﬁc con-

trol (ATC ) services are primarily manual open loop,

where the pilots in the aircraft and controllers on the

ground interactively make decisions based on the avail-

able information. The safety of the aircraft in the

air is provided by adhering to the established dis-

tance separation rules or separation minima in both

the horizontal and vertical domains. Thus, although

safety is assured, these separation requirements be-

tween aircraft often adversely impact the efﬁcient use

of airport and airspace capacity, thereby resulting in

ﬂight delays and creating extensive workload for the

humans operating the system. With the ever-growing

demand for air trafﬁc services, as these capacity re-

sources become scarce, there is a need to develop

closed-loop feedback control NAS capabilities that can

provide automated decision support to maximize capac-

ity, enhance ﬂight efﬁciency, and reduce pilot/controller

workload by minimizing reliance on cognitive decision

making.

This chapter describes the current NAS CNS/ATM

infrastructure, and the extent of the role that automation

plays in speciﬁc aircraft- and ground-based functions

for managing and controlling trafﬁc. The limitation of

these functions is discussed with their impact on ﬂight

safety, airspace/airport capacity, aircraft operational ef-

ﬁciency, and operator workload. The requirements for

the future air transportation system are presented, not

only to overcome the shortcomings of the current sys-

tem, but alsoto meet the service demandsof futureusers

effectively. The ongoing application of automated key

functions to meet the future system requirements are

presented to address hybrid automation/human decision

making in the cockpit and on the ground.

The scope of the contents presented here is limited

to an understanding of how the automated functions-

Part G 67

Air Transportation System Automation 67.1 Current NAS CNS/ATM Systems Infrastructure 1183

generated information helps manual decisions today,

and how automation will generate most decisions (ex-

cept those that are safety critical) in the future for

strategic planning and tactical operations during all

phases of ﬂight. Any consideration of the use of au-

tomation in design and control of the aircraft (except

for safe navigation and efﬁcient energy management),

or a discussion of the US Government programs to

upgrade and automate ground systems, is beyond the

scope of this chapter.

67.1 Current NAS CNS/ATM Systems Infrastructure

The current NAS infrastructure is comprised of a CNS

system architecture, and the AT M system that is de-

pendent upon an integrated system of airports and

ATC facilities. The ground-based ATM system includes

data/information acquisition and processing as well as

display capabilities supporting people in making deci-

sions to manage and control aircraft operations. The

CNS architecture deploys:

1. Very high-frequency (VHF) voice/data communica-

tion over the continental USA, and high-frequency

(HF) communication in the polar region and over

the ocean for air/ground communication

2. VHF omnidirectional range (VOR) and dis-

tance measuring equipment (DME): ground-based

navaids for navigation over pre-established routes,

with precision landing guidance to primary airports

provided by the instrument landing system (ILS),

NDBNDB

Navigation

ground-based NAVAIDS

ATM

Surveillance

radar

Communication

voice

Airline

dispatch

VHF

voice

VOR/DME

VHF/HF

voice

TRACON/ATCT

AT C

ILS

Fig. 67.1 NAS CNS/ATM infrastructure

and nonprecision landing guidance available from

VORs and nondirectional beacons (NDB)

3. Surveillance to locate and track the aircraft pro-

vided by the primary and secondary surveillance

radars (SSR). The weather radars detect and pro-

vide weather and wind information in some aircraft

cockpits and to the ground system.

The AT M system has two distinct functions: trafﬁc

ﬂow management (TFM)andAT C. The TFM func-

tion is involved in strategic planning of ﬂight operations

across the entire NAS based on the forecasted weather

and trafﬁc conditions throughout the airspace and at

all major airports. This is to balance the expected de-

mand with the airspace/airport capacity resources in

order to maximize operational efﬁciency and minimize

delays. There are, on average, 55000 daily operations

in the NAS. The AT C function provides tactical control

Part G 67.1

1184 Part G Infrastructure and Service Automation

of airborne aircraft and is designed to help controllers

maintain safe separation between aircraft. At an instant

of time, the AT C function controls up to 6500 aircraft

operations during peak trafﬁc conditions [67.1]. The

NAS-wide TFM function is located in the air trafﬁc

control system command center (ATCSCC), whereas

the regional and local TFM functionality is provided

by the trafﬁc management units (TMU) in the 20 air

route trafﬁc control centers (ARTCC) and the termi-

nal radar approach control (TRACON) facilities located

at the primary airports. The AT C functions for aircraft

movements at the airport surface and during depar-

tures/arrivals are handled by 400 air trafﬁc control

towers (AT CT ) located at the airports. The AT C func-

tions to separate aircraft up to about 30 nmi (nautical

miles) from the primary airport are provided by 185

TRACONs, and for ﬂights through the rest of the

airspace by the ARTCCs. The TFM functions interface

with the ﬂight operators through the airline dispatch

ofﬁces or the airlines operation centers (AOC)and

ﬂight service stations (FSS). The ATC functions require

direct interaction between the ATC facilities and the air-

craft [67.2,3]. Figure 67.1showsschematically the NAS

CNS/AT M infrastructure.

The US Federal Aviation Administration (FAA)

has classiﬁed the national airspace into six categories

in accordance with the ICAO airspace classiﬁcations.

Airspace classiﬁed as class A, B, C, D or E is des-

ignated as controlled airspace, where AT C services

are available, including air/ground communications,

navigation aids,and aircraft-to-aircraft separation assur-

ance. Class G airspace is uncontrolled airspace where

the AT C system is not responsible for managing trafﬁc.

Fig. 67.2 US VHF voice radio network

The following provides details on controlled airspace

classiﬁcations:

•

Class A: Class A airspace covers from 18 000ft

mean sea level (MSL) to 60000 ft MSL.

•

Class B: Class B airspace exists at 29 high-trafﬁc-

density airports in the US where the aircraft are

subjected to positive AT C, i.e., the aircraft need

ground control clearances to operate. Class B

airspace includes all airspace around these airports

from the surface up to 12000 MSL and spreads out

to about 30nmi.

•

Class C: Class C airspace exists around 120 air-

ports in the US that have control towers and radar

approach control. This class of airspace has two

concentric circular areas with a radius of 5 and

10nmi around the airport, and extends up to 4000ft

above ground level (AGL).

•

Class D: Class D airspace is a circular area of radius

5nmi around the airport and extends up to 2500ft

AGL at airports with an ATC T.

•

Class E: Class E airspace covers the volume of

airspace below the class A airspace from surface

to 18000 ft MSL, and excludes airspace covered by

classesB,C,andD.

67.1.1 Air/Ground Communications Systems

and Functions

Today the AT C communications are primarily two-way

voice capabilities over a radio system, which allow

the controllers and pilots to coordinate tactical ﬂight

maneuvers needed for safety. Fundamental to the com-

munication is the spectrum that supports it. For the ATC

communications, this spectrum is coordinated world-

wide, and is deﬁned in several distinct bands, each used

for speciﬁc purposes. Due to its long-range propagation

characteristics, HF communications at frequencies be-

tween 3 and 30MHz have been used in oceanic and

remote polar operations for many years. VHF spectrum

in the 117.95–137MHz band is set aside for opera-

tions in well-traveled airspaces that use a line-of-sight

radio infrastructure.Additional frequencies are setaside

for satellite communications, deﬁned and protected for

aeronautical communications in the 1.5 and 1.6 GHz

bands [67.4].

In the US, a network of VHF radio sites, shown

in Fig.67.2, provides the terrestrial infrastructure over

which the voice communications operate. Speciﬁc

frequencies in the band are provisioned to avoid in-

terference between the operating airspaces, based on

Part G 67.1