SmartCars and SmartRoads full report
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SmartCars and SmartRoads:
Applying technology of Wireless Sensor Networks to public transportation.

Thesis Proposal


Ricardo Joseph Estevez

As part of the requirements for the degree of

Master of Science in Computer Science

University of Colorado at Colorado Springs

1 Introduction

Human beings live in a world of advancing computing technology. Just as computers have entered every nook-and-cranny of our homes and workplace, so has the network. Today, a computer without a network is similar to a car without a road. We can drive around and use the functions provided in our car but without the road, how would we connect? Similarly without the network, our computers would be isolated and the dissemination of information limited. Overcoming this island of computing has led to the continuous linkage of computers to networks and the network’s ubiquitous integration into our daily lives.

The most famous network, which is actually a collection of interconnected networks or internetwork, is the Internet. The Internet, which is the backbone of the most popular distributed system known as the World Wide Web, has closed the geographic gap among people and businesses. It serves as a medium for researching, entertainment, business transactions, and communication .

A computer network provides a computational need with a large number of separate but interconnected computers . To be interconnected means to be able to exchange information over some connection such as copper wire, fiber optics, microwave, infrared, and communication satellites. The observation is that the connection medium is both wired and wireless.

A specialized networking technology known as wireless sensor networks (WSN) is providing researchers with remote monitoring capabilities. The application area of wireless sensor networks is virtually the world around us. Wireless sensors can monitor the environment and gather measurements such as temperature, light, and seismic readings. These sensing devices, called motes, can self-organize to form a wireless network enabling the transmission of their measurements to a central processing unit for aggregation, analysis, and decision-making. For example, WSN have been used to perform habitat monitoring at Great Duck Island in Maine. In another example, implementing WSN allows first responders to perform risk analysis on a building structure and to track personnel once deployed into a distressed building.

The domain of public transportation can benefit from wireless sensor networks. This is a popular topic in the automotive industry. For the government, the data acquired from the wireless sensor networks could pinpoint hazards in the road related to road decay or weather conditions. For insurance companies, the data could be used to determine the cause of an accident. And for the consumer, automobile manufacturers could use the data to create safety or convenience systems. In this research, the focus is the consumer.

This thesis provides analysis, simulation, and feasibility of algorithms for passive and active safety systems that are built upon an architecture that is founded heavily on the application of wireless sensor networks to automobiles and the roads they travel.

The rest of this paper is organized as follows. Section 2 formally states the details of the thesis. Section 3 provides background information of wireless sensor information including its constraints, hardware, and software architecture. Section 4 examines the application of wireless sensor networks to public transportation. This examination is important because it will be shown that there is a lack of focus in the area of safety systems for the consumer. Section 5 presents a high-level object-oriented design to identify actors, objects, and system boundaries. Section 6 outlines the thesis project and implimentation plan and schedule. Section 7 lists the thesis project and implimentation deliverables. Section 8 cites the references.

2 Thesis

The technology of wireless sensor networks can be integrated into automobiles and roadways to provide the following safety systems:
1. Forward-looking traffic detection algorithm.
2. Emergency braking alert with tailgating awareness algorithm.

2.1 Thesis Objectives

• Identify the safety systems that can be created when wireless sensor networks enter the automobile and roadways.

• Identify algorithms that will use data provided by the wireless sensor network in order to make decisions that are used in the safety systems.

• Investigate existing hardware platforms provided by wireless sensor technology vendors such as Crossbow for compatibility with the safety systems presented in this paper.

• Investigate how to best deploy motes such that the radio signal obstruction, propagation, and attenuation factors are addressed.

• Investigate the safety system’s performance and scalability in varying bandwidth, which directly affects the transportation of data. Increased data flow in high traffic situations could possibly hinder bandwidth and thus the propagation of data. Identify networking algorithms to ensure the dissemination of current traffic data. Packets will likely need a priority and maturity algorithm.

2.2 Proof-of-Concept and Simulation

The TinyOS, or TOSSIM, simulator will be used to test the mote system programming, deployment strategies, and safety-decision algorithms. Empirical data on traffic patterns will be incorporated as well.

3 Wireless Sensor Networks

A wireless sensor network is composed of the sensing devices called motes. Motes will receive and send data to a base station for aggregation so that observations and decisions can be made. Characteristics of a wireless sensor networks include the ability to make autonomous actions based on environmental observations. Motes need to be self-organizing, self-regulated, self-repairing, and programmable. The mote technology is somewhat constrained in order to provide a low-cost, reusable deployment into varying environments. For example, a mote has limitations on physical memory, computation power, bandwidth, and energy. Another constraint is size. Some motes only the size of a coin will be able to be deployed into an environment by the millions thus creating a “digital skin…with each sensor capable of capturing physical information about its immediate space.” The raw data can then be aggregated to provide data points that can be analyzed to derive a meaningful observation.

4 Public Transportation

Public transportation is a domain area that many researchers are examining for the full-scale integration with wireless sensor networks. Here we examine the existing studies at research laboratories and academic institutions.

One researcher, Teresa Lunt, who is the manager of the Palo Alto Research Center’s computer science lab envisions a sensor network where “cars can communicate with one another on the highway…the Transportation Department has been interested in reducing the number of accidents by putting sensor on vehicles that could pre-deploy your airbag [or] a merge assistant that could tell you if someone’s in your blind spot or around the corner, or if you’re driving over the road. All these things could be enabled through sensors.”

The CarTel project and implimentation at the Massachusetts Institute of Technology believes that with hundreds of millions of vehicles on the road and over a billion people using cellular phones, “cars and humans may turn out to be the carriers of the world’s largest and most dynamic sensor networks in the coming years.” This project and implimentation has designed and deployed a mobile distributed sensor computing system. The system has a three-tier architecture, thus creating a layer for the database, business logic, and client. Conceptually, the system works as follows: the client layer presents a thin-client portal where asynchronous queries can be sent to the mobile sensor nodes deployed on a vehicle. The network on which these queries travel is designed to be delay-tolerant since the sensor nodes create the mobile network. When the query reaches the mobile sensor node, it provides the query along with the parameters, such as response priority, so the data collected can travel back through the network with assigned priority and destination. Finally, the data returns and is inserted into a relational database where another client application can view statistics visually with map data. As innovative as the CarTel project and implimentation is, it still lacks the investigation of safety systems that will benefit the automotive industry. Instead, the project and implimentation focused usage for their system is traffic monitoring and route planning; preventive maintenance; road condition monitoring; or a proxy monitoring service for other wireless networks.

This thesis builds on the ideas similar to the studies at Palo Alto and MIT. Note that these areas of research have focused on efforts similar to environmental monitoring. However, this thesis focuses exclusively on the application of the wireless sensor networks and provides innovative algorithms to create new safety systems for the driver and cars. In Japan, Honda is already testing the integration of advanced safety systems into cars and roads . Their prototype safety system features the capability to allow a car to send and receive signals from other cars and the road. Such signals could warn of road hazards; a red-light runner at a traffic light intersection; school zone warnings; and traffic data.

The research conducted in this thesis has led to the creation of several algorithms and architecture to provide passive and active systems that will enable automobiles and drivers with traffic data. The concepts in this study are different than similar studies because of the following constraints:
• Build a non-proprietary system.
• Build an affordable system using inexpensive but reliable computing devices.
• Build a system that is wireless sensor network centric.
• Build a system that can leverage existing technologies such as GPS.

In order to formally analyze the problem domain of public transportation, object-oriented analysis will be used to identify and define the objects and data.
5 High-level system design

The safety systems presented in this study depends on the SmartCar and SmartRoad subsystems.

SmartCar – an automobile that has the capability to interpret traffic data aggregated by the WSN to make life-saving safety decisions such as accident detection and accident avoidance. A SmartCar will also be a source of data for other SmartCars to interpret.

SmartRoad – a city street, state highway, or national interstate that has motes deployed at mile markers, lane striping, and other static road artifacts.

Both subsystems will employ a packet-relaying algorithm that will degrade old traffic data and promote for removal so that network will contain current traffic data.

The figure shown below is a visual description of the high level design. Conceptually, the SmartCars will communicate to each other over the wireless sensor network passing data that will allow another SmartCar to make a decision supporting the on-board active and/or passive safety systems. The SmartRoads will relay data downstream.

6 Thesis Plan & Schedule

(see attached file)

7 Deliverables

At the end of the Fall Semester 2006, I will successfully complete my thesis research and deliver the following artifacts to my graduate committee:
1. Thesis report.
2. A simulator that demonstrates the concepts and algorithms of the smart car/road system.

8 References

1. A. Ricadela. The Emerging World Of Wireless Sensor Networks. Information Week, January 20, 2005.

2. Wireless Ad Hoc Sensor Networks. Advanced Network Technologies Division, National Institute of Standards and Technologies.

3. H. Balakrishnan, S. Madden, V. Bychkovsky, K. Chen, W. Daher, M. Goraczko, H. Hu, B. Hull, A. Miu, E. Shih. CarTel: A Mobile Sensor Computing System. Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

4. Energy Efficient Sensor Networks (EYES). Telecommunications Networks Group, Technical University of Berlin, Germany.

5. Wireless Senor Networks: a Fragmented Market with Great Potential., April 14, 2006.

6. OMNeT++ Discrete Event Simulation System.

7. G. Smit, P Havinga. Collaborative Sensor Networks. Department of Computer Science, University of Twente, Netherlands.

8. A. Mainwaring, J. Polastre, R Szewczyk, D. Culler, J. Anderson. Wireless Sensor Networks for Habitat Monitoring. In WSNA ’02, Atlanta, Georgia, USA, September 2002.

9. D. M. Doolin, N. Sitar. Wireless sensors for wildfire monitoring. Civil and Environmental Engineering, University of California, Berkeley, CA, USA.

10. C. E. Chow, G. Godavari. First Responders Sensor Network. Department of Computer Science, University of Colorado at Colorado Springs, Colorado Springs, CO, USA, Fall 2003.

11. Crossbow MoteWorks™ Platform. Crossbow Technology Inc.

12. MSP410 Wireless Security System Specification Sheet. Document Part Number: 6020-0064-01 Rev B. Crossbow Technology Inc.

13. MSP-SYS MSP Mote Developer’s System Specification Sheet. Document Part Number: 6020-0084-02 Rev A. Crossbow Technology Inc.

14. B. Leiner, V. G. Cerf, D. D. Clark, R. E. Kahn, L. Kleinrock, D. C. Lynch, J. Postel, L. G. Roberts, S. S. Wolff. The past and future history of the Internet. In Communications of the ACM, February 1997.

15. A. Tanenbaum. Computer Networks. Prentice Hall PTR, Upper Saddle River, New Jersey 07458, 2003.

16. C. Woodyard. Cars soon may ‘talk’ to roads, each other. USA Today, November 2005.

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15-03-2011, 09:05 PM

Smart Cars
Athul Vijay & Arjun Sreenivas
S4, Department of Mechanical Engineering
Mohandas College of Engineering and Technology

.pdf   Smart Cars.pdf (Size: 187.41 KB / Downloads: 52)

Science is a long way from producing machine as powerful as the human brain. However, the
search fo artificial intelligence has come a long way since the first robots. New technologies
enables scientists to produce devices capable of a range of human-like action, while many
scientists now look to the insect world for inspiration for tomorrow’s thinking machines. This
paper aims at three basic concepts of driving that is vehicle efficiency, driver comfort & eco-
friendliness. The future is not something that we enter’ but we create. So, the smart car.
Smart cars just don’t mean cars that run on artificial intelligence. It’s a combination of works
assembled to make a masterpiece. Imagine a car with high efficiency, a car that can ease the
driver stress, increase the safety & finally be eco-friendly, when all this comes in one bundle we
get the smart cars. Artificial intelligence (AI) is the intelligence of machines and the branch of
computer science that aims to create it. The study and design of intelligent agents,"where an
intelligent agent is a system that perceives its environment and takes actions that maximize its
chances of success. Artificial intelligence has been the subject of optimism, but has also suffered
setbacks and, today, has become an essential part of the technology industry, providing the heavy
lifting for many of the mostdifficult problems in computer science. AI research is highly technical
and specialized, deeply divided into subfields that often fail to communicate with each other. In
this paper we are discussing about the impacts of ai in automobile industry. I-car is the latest
emerging trend using ai as the base of operation. Most of the time, smart cars are mistaken with
hybrid vehicles, smart cars are vehicles that use the latest technologies along with ai & other
ultra modern technologies to ease human control over vehicle

Adaptive Cruise Control
Autonomous cruise control is an optional
cruise control system appearing on some
more upscale vehicles. The system goes
under many different trade names according
to the manufacturer. These systems use
either a radar or laser setup allowing the
vehicle to slow when approaching another
vehicle and accelerate again to the preset
speed when traffic allows. ACC technology
is widely regarded as a key component of
any future generations of intelligent cars.
Laser-based systems are significantly lower
in cost than radar-based systems; however,
laser-based ACC systems do not detect and
track vehicles well in adverse weather
conditions nor do they track extremely dirty
(non-reflective) vehicles very well. Laser-
based sensors must be exposed, the sensor (a
fairly-large black box) is typically found in
the lower grille offset to one side of the
vehicle. Radar-based sensors can be hidden
behind plastic fascias; however, the fascias
may look different from a vehicle without
the feature. For example, Mercedes
packages the radar behind the upper grille in
the center; however, the Mercedes grille on
such applications contains a solid plastic
panel in front of the radar with painted slats
to simulate the slats on the rest of the grille.
Radar-based systems are available on many
luxury cars as an option for approx. 1000-
3000 USD/euro. Laser-based systems are
available on some near luxury and luxury
cars as an option for approx. 400-600
USD.Two companies are developing a more
advanced cruise control that can
automatically adjust a car's speed to
maintain a safe following distance. This new
technology, called adaptive cruise control,
uses forward-looking radar, installed behind the grill of a vehicle, to detect the speed and
distance of the vehicle ahead of it.Adaptive
cruise control is similar to conventional
cruise control in that it maintains the
vehicle's pre-set speed. However, unlike
conventional cruise control, this new system
can automatically adjust speed in order to
maintain a proper distance between vehicles
in the same lane. This is achieved through
a radar headway sensor, digital signal
processor and longitudinal controller. If the
lead vehicle slows down, or if another object
is detected, the system sends a signal to the
engine or braking system to decelerate.
Then, when the road is clear, the system will
re-accelerate the vehicle back to the set
speed.The 77-GHz Autocruise radar system
made by TRW has a forward-looking range
of up to 492 feet (150 meters), and operates
at vehicle speeds ranging from 18.6 miles
per hour (30 kph) to 111 mph (180 kph).
Delphi's 76-GHz system can also detect
objects as far away as 492 feet, and operates
at speeds as low as 20 mph (32
kph).Adaptive cruise control is just a
preview of the technology being developed
by both companies. These systems are being
enhanced to include collision warning
capabilities that will warn drivers through
visual and/or audio signals that a collision is
imminent and that braking or evasive
steering is needed. The cruise control system
actually has a lot of functions other than
controlling the speed of your car. For
instance, the cruise control pictured below
can accelerate or decelerate the car by 1 mph
with the tap of a button. Hit the button five
times to go 5 mph faster. There are also
several important safety features -- the
cruise control will disengage as soon as you
hit the brake pedal, and it won't engage at
speeds less than 25 mph (40 kph).
BMW 7 Series, 5 series, 6 series, 3 series,
Audi A4

Lane Departure Warning System
If the car concludes that the driver is
drowsing (more on that later), it issues an
audible alarm, and an icon depicting a cup
of coffee flashes on the instrument panel The company's Driver Attention Warning
System uses a voice alarm: If a driver is
nodding off, the car announces "You are
tired," followed by "You are dangerously
tired! Stop as soon as it is safe to do so!"
The driver's seat also vibrates to help rouse
him or her. Additional measures, like
emitting puffs of air on the back of a dozing
driver's neck, vibrating steering wheels and
automatic steering that takes over and gently
uides you back into your lane when you
drift, may all be found in driver alert
systems soon. How can a car tell when
you're nodding off? Researchers are
tweaking already extant car safety
technologies and applying them in new
ways. For example, blind-spot warning
systems in today's digital cars keep an eye
out for other vehicles in places you can't see.
They also analyze your car's relation to its
lane and whether your turn signal's on or
not. Add to this system automatic steering
that kicks in when you drift, and you've got
part of a drowsy driver alert system. onboard
computer uses facial recognition software to
determine if you're drowsing. Night vision
cameras trained on your face analyze
slackening facial muscles, your blinking
patterns and how long your eyes stay closed
between blinks. Once it concludes you're no
longer awake, the system kicks in to rouse
you from your dangerous slumber.

Adaptive Highbeam
Adaptive Highbeam Assist is the newest
headlamp technology, introduced in 2009 in
the new generation Mercedes-Benz E-Class.
It is based on camera mounted behind the
windshield and automatically and
continuously adapts the headlamp range to
the distance of vehicles ahead or which are
oncoming. The same technology is also
present in the BMW 7 series. BMW's
version of this technology, developed in
cooperation with Mobileye, uses swiveling
headlights that always point in the direction
the vehicle is steering so therefore the road
ahead is better illuminated and obstacles
become visible sooner. Adaptive Highbeam
Assist is the newest headlamp technology,

1.Safety first: VDIM puts Toyota at the head
of the safety technology pack in Japan |
Automotive Industries | Find Articles at
2.”Revolutionary Lane departure system”
-Dr Kaimen James Jr.
3.AMC Smart Cars ,Mark Donohue
4.The Car That Changed The World , Bruce
W. McCalley


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