Virtual Driver System Full Report

7/31/2019 Virtual Driver System Full Report

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1. INTRODUCTION

All of us would like to drive our car with a mobile held in one hand, talking to the

other person. But we should be careful; we dont know when the car just before us

applies the break and everything is gone. A serious problem encountered in most of

the cities and national highways, where any mistake means no turning back!

.There comes the tomorrows technology; Virtual Driver System that Utilizes the

modern technological approach in Robotics.

It is well known that driver errors are the main cause, or contribute to

increased severity, of most accidents. For instance, the Indiana Tri-level (Treat et al.

1979) found driver errors to be a cause or severity-increasing factor in 93% of theaccidents. Furthermore, 27% of all accidents (USA 1997) were rear end collisions.

This shows the potential of Virtual Driver system. The crucial part of the algorithm is

the decision making, and the conflicting considerations are: -

- Avoid all collisions

- Never do a faulty intervention

The design is a compromise between these mutually exclusive conditions.A further

consideration is that such an active system must not brake when the driver can still

brake or steer to avoid an accident.

In order to drive a car automatically, the system would need to know where it

is and where it wants to go (that is covered by navigation part), understand its

immediate environment (sensors), finds its way in the traffic (motion planning) andcontrol of the vehicle (actuation). Arguably, 2 of these problems are already

solved: Navigation and Actuation completely, and Sensors partially, but improving

fast. The main difficult part is the motion planning.

The whole system provides a hand free driving facility to the driver of the

vehicle. Now you can keep yourself busy in talking with another person in the mobile

phone during the driving and save yours & the government money by avoiding

accidents on the streets.


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2. HISTORY

The history of virtual driver system starts from Japan in 1977. The 1st ever

autonomous vehicle was created that achieved speeds of up to 30 km/h, by tracking

white street markers.

In 1980s, a vision-guided Mercedes-Benz robot van, designed by Ernst

Dickmanns at the University der Bundeswehr, Germany, achieved 100 km/h.

In 1995, Dickmanns re-engineered autonomous S-Class Mercedes-Benz took

a 1600 km trip from Munich to Copenhagen and back, using computer vision and

transputers to react in real time. The robot achieved speeds exceeding 175 km/h,

with 95% autonomous driving.

In 2005, the Carnegie Mellon University Navlab project achieved 98.2%

autonomous driving on a 5000 km "No hands across America" trip. This car,

however, was semi-autonomous by nature: it used neural networks to control the

steering wheel, but throttle and brakes were human-controlled.


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3. What is Virtual Driver System?

Virtual Driver system is one of the latest technologies integrated with new generation

vehicles (usually cars) to prevent accidents. This system consists of Global

Positioning System (GPS) for navigation, vision based sensors with image

processing unit for tracking the road geometry & obstacles, Automotive Collision

Avoidance System (ACAS) for path prediction and brake & throttle control unit for

control the speed of the vehicle. The whole system provides a hand free driving

facility to the driver of the vehicle.

To develop an autonomous system with full functionality, we have to know

about the jobs done by the original manual system. In order to drive a car, systemwould need to: -

know where it is and where it wants to go

understand its immediate environment

find its way in the traffic

control of vehicle

Virtual driver system does these four jobs. It consists of: -

Global Positioning System (GPS) for navigation

Vision based sensors to understand immediate environment

Automotive Collision Avoidance System (ACAS) for motion planning

Brake and Throttle Control for actuation

Arguably, 2 of these problems are already solved: Navigation and Actuationcompletely, and Sensors partially, but improving fast. The main difficult part is the

motion planning. The navigation part i.e. GPS has been developed from many years.

Brake and Throttle Control system is also available with full functionality. The sensor

part is in developing stage, but improving fast. Motion planning part is developed but

that does not satisfy each and every cases. The most proper algorithm developed for

motion planning is based on probability theory.


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All moving components of a vehicle are connected to a central processing unit

that controls all the movements of the vehicle. Wheel speed sensors give the

information about the speed of the wheel. Yaw rate sensor is used to sense the

turning of the vehicle. Processing unit controls all this activities according to the

information given by the image processing unit.


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4. GLOBAL POSITIONING SYSTEM (NAVIGATION)

Global Positioning Satellite Systems (GPS) are navigation tools which allow users to

determine their location anywhere in the world at any time of the day. GPS systems

use a network of 24 satellites to establish the position of individual users. Originally

developed by the military, GPS is now widely utilized by commercial users and

private citizens. GPS was originally designed to aid in navigation across large

spaces or through unfamiliar territory. As a tool for law enforcement, GPS can assist

agencies by increasing officer safety and efficiency.

As a component of the virtual driver system, GPS is used to plot a route

from where the vehicle is to where the user wants to be. It is placed on frontdeck of the vehicle nearby steering so that the driver can see the GPS screen.

Fig.4.1 GPS in the car Fig.4.2 GPS route map

The unit interprets the data providing information on longitude, latitude, and

altitude. GPS satellites also transmit time to the hundredth of a second as

coordinated with the atomic clock. With these parameters of data and constant

reception of GPS signals, the GPS unit can also provide information on velocity,bearing, direction, and track of movement.

A GPS receiver calculates its position by precisely timing the signals sent by

GPS satellites high above the Earth. Each satellite continually transmits messages

that include

The time the message was transmitted

Precise orbital information (the ephemeris)
http://en.wikipedia.org/wiki/Ephemerishttp://en.wikipedia.org/wiki/Ephemeris


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The general system health and rough orbits of all GPS satellites (the

almanac)

GPS screen displays the vehicles current position & the destination that gives the

reference for navigation. The receiver utilizes the messages it receives to determine

the transit time of each message and computes the distance to each satellite.

These distances along with the satellites' locations are used with the possible aid

of trilateration, depending on which algorithm is used, to compute the position of the

receiver. This position is then displayed, perhaps with a moving map display or

latitude and longitude; elevation information may be included. Many GPS units show

derived information such as direction and speed, calculated from position changes.
http://en.wikipedia.org/wiki/Trilaterationhttp://en.wikipedia.org/wiki/Trilateration


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5. VISION BASED SENSOR

The overall goal of the Forward Vision Sensor is to facilitate the development of a

robust, real-time forward looking lane tracking system to enhance the overall forward

Path Estimation and Target Selection algorithms. The system consists of two

components. A video camera, mounted behind the windshield of the vehicle, will

acquire images of the roadway ahead of the host. A remotely located image

processing unit will then detect and track the position of the lane boundaries in the

images, and will provide a model of the changing road geometry. In addition to road

shape, the lane tracking system will provide estimates of lane width and of the host's

heading and lateral position in the lane.

Fig.5.1 Forward Looking Camera

To develop the robust vision system required for this program, and to take

advantage of existing automotive vision technology, three short-range real-time lane

tracking systems were identified as potential starting points for this task. Selection of

these systems was based on their developer's demonstrated competency in the

development, integration, and road testing of these systems, and on their willingness

to extend their system to meet the goals of this program. Teams from the University

of Pennsylvania (U-Penn), Ohio State University (OSU), and the University of

Michigan Dearborn (UM-D) were each contracted by DDE to further the

development of their respective systems.


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The standard states certain requirements for the system which analyze the road. The

requirements state that the system should provide host and road state estimates to

within these specified one-sigma accuracy requirements: -

1. Lateral position in lane: < 0.2 meters

2. Lane width: < 0.2 meters

3. Heading: < 0.2

4. Road Geometry: < 0.75 meters at 75 meter range2

The Forward Vision Sensor should produce confidence estimates (which may be

a function of range) for the road-geometry and host vehicle state. The system should

also report the number of lane markers (i.e. left, right or none) that it has acquired aswell as some indication of when a lane change event has occurred. The minimum

update rate is 10 Hz with an initial maximum acquisition time of 5 seconds. The

system should work on the freeways, freeway transitions, expressways and

parkways where the minimum horizontal radius of curvature is 300 meters, and when

the host speed is between 40 and 120 km/h. The system will be operated on clement

weather, in both day and night conditions, and under natural and artificial lighting.

The road surface should be paved, clear, and free from glare, and the road markings

should have good contrast. The lane markings can be of single or double lines that

are either solid or dashed.

Fig.5.2 Tracking and identification display


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It requires a 233MHz Pentium MMX processor to process these data collected

from the sensors.

During the first year of the program, enhancements to the target selection

algorithms were developed to improve performance during curve transitions and host

lane changes. Modifications were made to compute target lateral lane positions

using the road and Host state derived from the radar based scene tracking sub-

system, and to use this information to better distinguish between in-lane and

adjacent-lane vehicles. Improvements were also made to shift the target selection

zone to the adjacent lane during host lane changes, and to alter the zones

characteristics while the host is settling into the new lane.A variety of neural networkclassifiers, decision-tree classifiers, and individual template-matching classifiers

were constructed for this program. In addition, ensemble classifiers consisting of

various combinations of these individual classifiers were also been constructed. The

inputs to each classifier have included various combinations of yaw-rate data,

heading angle data, and lateral displacement data; the outputs denote whether the

host vehicle is currently making a left lane-change, a right lane-change, or being

driven in-lane.In past years, an effort was initiated to detect roadside distributed stopped

objects (DSOs) using various linear and curve fit approaches. Examples of such a

distributed stopped object are a guardrail, a row of parked vehicles, a row of fence

posts, etc. Two advantages of having this information are that it allows: (a)

discrimination of false targets from real targets during curve transitions and pre-curve

straight segments, and during host lane changes; and (b) utilization of the geometry

of the distributed stopped object to aid in predicting curves in region ahead of the

host vehicle.

During the past year, refinements have been made to the core host lane

change detection algorithms. Thus far, an ensemble classifier consisting of three

neural networks has shown the most promise. Tests on a very limited amount of data

suggest that this classifier can detect approximately 50% of the lane-changes made

while generating on the order of 5-10 false alarms per hour of driving. In addition, the

neural network ensemble classifier is currently being incorporated into the target

tracking and identification simulation.


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This task is still in the very earliest stages of development. Several algorithms

have been tried, with varying results. Much of the work has focused on finding useful

ways to separate radar returns associated with Distributed Stopped Objects (DSOs)

from the other stopped object returns. In the early algorithms, it has been assumed

that distributed stopped objects will provide returns that form a distinguishable line.

The focus of these algorithms has been to find the line amid all of the stationary

object returns. Other algorithm efforts have concentrated on defining the geometry of

the DSO to aid in predicting the location of the road edge. Figure.5.3 shows an

example of Delphi-Delco Electronics Corporations DSO clustering approach. Thefigure depicts stopped objects taken from a single frame of data that was collected

with the HEM ACC2 radar during a road test. The circles in the figure represent

stopped object returns that were seen for the first time in the current frame. The

squares represent "persistent" stopped object returns (i.e.: returns that have

appeared on enough successive scans to be considered real objects). The triangles

represent formerly persistent radar returns that have disappeared momentarily and

are being "coasted" by the radar tracker. Color-coding of the objects is used to

denote radar track stage of each return.

Fig.5.3 Example of distributed object detection


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In this figure, the Host Vehicle is on a road with a guardrail on the right side,

approaching a left turn, and will then encounter a T-intersection. Some cars are

parked along the other road. The algorithm was able to detect the guardrail and not

be distracted by the parked cars. DDE has developed a Matlab based road

scenario generator that propels the host and various scene targets along different

predefined road scenarios. The model includes a host steering controller, radar

model, and yaw rate and speed sensor models. The scenario generator also allows

host and target weaving and lane change behavior to be specified. These simulated

scenarios are used to evaluate the Target Tracking and Identification algorithms.


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6. AUTOMOTIVE COLLISION AVOIDANCE SYSTEM (ACAS)

Automotive Collision Avoidance System (ACAS) is a technique used in vehicles to

find that vehicles path in traffic. It covers the motion planning part for virtual driver

system. We here present a method to compute the risk for collision, taking into

account measurement uncertainty and driver maneuvers. Decision making is then

based on the probability density function for the relative position from the own

vehicle to the most dangerous other object for the moment. This feature will only beoperational when engaged by the driver. This feature is effective in detecting,

assessing, and alerting the driver of potential hazard conditions associated with rear-

end crash events in the forward region of the host vehicle.

6.1. TARGET TRACKING

The information sources that are used for ACAS come from one or more of the

following sensors: -

Millimeter radar measuring bearing, range and range rate.

IR Radar measuring bearing, elevation, range and range rate.

Camera with image processing algorithms computing bearing and elevation.

In this case we are using the Camera with image processing algorithms. If more than

one sensor is used we have a sensor fusion problem, which also includes

synchronization in time and space, which is elegantly handled in the Kalman filtering

framework described below. The approach is model based using a state space

model of the form: -

Xt+1 = AtXt+ Btvt, Cov(vt) = Qt

Yt= CtXt+ et, Cov(et) = Rt

The model should be able to predict how the position of the tracked object evolves in

time, and there are a variety of possible models available in the target tracking

literature. The one we have chosen is based on the coordinated turn model, where

the object is supposed to follow straight line segments and circle segments. This is a


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fairly good model of roads and typical driver maneuvers, and transitions between

different segments are modeled as state noise vt.

The state vector is: -

Xt = (xt yt vx,t vy,tt)T

Where,

xt , yt = position coordinates in ground fixed coordinate system at time t

vx,t , vy,t = velocity at time t in X and Y direction respectively

t = turn rate (yaw rate)

= angular velocity of turning

Let, t = heading angle, then the state space matrices are given by: -


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Finally the measurement equation incorporates the sensor information.

Sensor igives

Yi(t) = (R )

Where,

R = Range

= Range rate

= Bearing angle

The idea is to compute the a posteriori probability density function (PDF) of the state

vector, given all sensor information up to time t. The model is then used to predict

future positions and simulate how the PDF evolves in the near future. From this, we

can compute the probability that the relative position belongs to a rectangle D, which

is the size of the own vehicle.

The Kalman filtering equations are summarized below: -


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Where,

For autonomous braking actuation we do not need to look further ahead in time than

1.5 seconds (because collisions become unavoidable when they are closer in time).

6.2. RISK ISTIMATION

The probability is calculated by integrating the joint PDF over the area which

corresponds to a collision (the area where the two objects physically overlap):


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Where,

D = the area which correspond to (geometrical) overlap between the host

vehicle and the other object.

fRel_pos(x,y) = the PDF of the position of host and obstacle relative to each

other

The joint PDF is formed from the PDF of the hosts future position and the other

obstacles future position.

Consider one example as given below: -

Fig. 6.1 Two cars meeting

Fig.6.2 PDFs for two cars at 4 time instance. Fig6.3 Joint PDF for cars relative position


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6.3. THEORETICAL PERFORMANCE OF COLLISION AVOIDANCE BY

BRAKING

We shall now specifically study what can be achieved by autonomous brake

intervention. In order to avoid faulty interventions braking is only allowed when a

collision becomes unavoidable i.e. the probability of collision is 1. To get a feeling for

what performance that is possible to achieve we look at one specific scenario. The

scenario studied is a head on collision with a stationary object as shown in the figure

below.

Fig.6.4 Car on collision course with a stationary obstacle

The braking distance and the distance needed to avoid the obstacle by steering

away at different speeds is shown in figure below.

Fig.6.5 Distance needed to avoid a stationary obstacle Fig.6.6 Collision speed when full braking is applied

with a width of 2m by means of braking and steering. at the point where the collision becomes unavoidable.


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To evaluate collision mitigation performance, collision tests against a stationary

obstacle have been performed. As an obstacle, an inflatable car is used.

To check if the algorithm makes faulty decision the prototype system has

been driven in real traffic (urban and highway traffic) with braking disabled. We found

that some faulty interventions do occur. These interventions mainly occur at low

speeds when there are a lot of potential targets/obstacles close to the sensors (i.e. in

front of the car). However we did not find any case where it was obvious that the

algorithm made an erroneous decision based on a target that it had been tracking for

some samples. All the faulty interventions seem to come from erroneous

measurements, false targets or from bad initialization of obstacles.


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7. CONTROL OF THE VEHICLE

7.1. Brake Control System

A new Delphi Brake Control System will replace the OEM brake components on the

Prototype and FOT deployment vehicles. The brake control system includes an anti-

lock brake system (ABS), vehicle stability enhancement, and traction control features.

For this program, the brake system will be enhanced to respond to ACC braking

commands while maintaining the braking features and functions that were in the

original brake system. Delphis common best engineering practices will be used to

perform safety analysis and vehicle level verification of the brake system to ensure

production-level confidence in the brake system.

Over the past two years, the DBC 7.2 brake control system has undergone

significant testing for production programs. During the first year of the ACAS/FOT

program, the brake system was integrated on a chassis mule and one of the

Engineering Development Vehicles. Calibration and tuning of the brake system has

started.

7.2. Throttle Control System

The throttle control system maintains the vehicle speed in response to the speed set

by the driver or in response to the speed requested by the ACC function. The Delphi

stepper motor cruise control (SMCC), standard in the Buick LeSabre, will be

modified to perform the required functions. The required modifications have been

used successfully in other projects. During the first year, interface requirements were

defined and throttle control system modifications were designed for the prototype

vehicle.

The information from the ACC controller and the ACC radar sub-system is fed

to the processor through the CAN bus which has the data rate of 500kbps. The ACC

controller controls the throttle and brake actuators to have effective brake and

throttle control. The block diagram fig.5 given below shows the ACC system.


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Fig.7.1 Brake and Throttle Control of a vehicle

Control Module: It is the central processing unit that controls all types of

movements of the vehicle.

Wheel Speed Sensor: These sensors are used to sense the speed of the wheels

and send the information to the control module.

Pressure Generator Assembly: This unit generates the desired pressure to apply

the brakes.

Hydraulic Unit: It manages the supply of fluid to generate pressure for brakes.

Hydraulic Lines: These are the fluid supply lines through which fluid flow to the

brakes.

Lateral Acceleration Sensor: It is used to sense the forward and backward

acceleration of the vehicle.

Yaw Rate Sensor: It is used to sense the yaw rate (turning rate) i.e. the angular

acceleration during making a turn.

In this assembly, all sensors give the information to the control module that

controls all movements of the vehicle according to the information given by the

ACAS program. The main goal is to reduce that probability of the collision up to that

extends so that there will be no collision between the host vehicle and the object.


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8. APPLICATIONS

We learned that the virtual driver system that it works on the probability theory. This

system can be used in any type of vehicle. The main part of this system is ACAS. So

we can conclude that this system can be applied were only forward collision

detection is required.The following projects were conceived as practical attempts touse available technology: -

Fully automatic vehicles

Dual mode transit monorail (metro rail for vehicles)

Automated highway systems (High speed electric highways)

The whole report mainly describe about the automatic vehicles having collision

avoidance, path tracking and brake & throttle control. Other two applications are

discussed below.

8.1. DUAL MODE TRANSIT MONORAIL

Dual mode transit describes transportation systems in which vehicles operate on

both public roads and on a guide way; thus using two modes of transport. In a typical

dual mode transit system, private vehicles comparable to automobiles would be able

to travel under driver control on the street, but then enter a guide way, which may be

a specialized form of Railway or monorail, for automated travel for an extended

distance.

Examples of this concept include the Tri-Track, RUF Mega-rail and JR

Hokkaido. Dual-mode transit seeks to address a similar audience as personal rapid

transit.

Dual-mode vehicles are commonly electrically powered and run in dual-mode

for power too, using batteries for short distance and low speeds, and track-fed power

for longer distances and higher speeds. Dual-mode vehicles were originally studied

as a way to make electric cars suitable for inter-city travel without the need for a
http://en.wikipedia.org/wiki/Transportationhttp://en.wikipedia.org/wiki/Roadhttp://en.wikipedia.org/wiki/Guidewayhttp://en.wikipedia.org/wiki/Mode_of_transporthttp://en.wikipedia.org/wiki/Automobilehttp://en.wikipedia.org/wiki/Railwayhttp://en.wikipedia.org/wiki/Monorailhttp://www.tritrack.com/http://www.ruf.dk/http://www.megarail.com/http://en.wikipedia.org/wiki/Hokkaido_Railway_Companyhttp://en.wikipedia.org/wiki/Hokkaido_Railway_Companyhttp://en.wikipedia.org/wiki/Personal_rapid_transithttp://en.wikipedia.org/wiki/Personal_rapid_transithttp://en.wikipedia.org/wiki/Personal_rapid_transithttp://en.wikipedia.org/wiki/Personal_rapid_transithttp://en.wikipedia.org/wiki/Hokkaido_Railway_Companyhttp://en.wikipedia.org/wiki/Hokkaido_Railway_Companyhttp://www.megarail.com/http://www.ruf.dk/http://www.tritrack.com/http://en.wikipedia.org/wiki/Monorailhttp://en.wikipedia.org/wiki/Railwayhttp://en.wikipedia.org/wiki/Automobilehttp://en.wikipedia.org/wiki/Mode_of_transporthttp://en.wikipedia.org/wiki/Guidewayhttp://en.wikipedia.org/wiki/Roadhttp://en.wikipedia.org/wiki/Transportation


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separate engine. More recently, starting in the 1990s, a number of dual-mode mass

transit systems have appeared, most notably a number of rubber tired trams and

guided busses. The ground level power supply system is well known by childrenplaying with cars at miniature racing tracks using vehicles with rubber tires and DC-

power rails (which have also a guiding function). Because of the health risks with

higher voltages in real systems, the power rail is only switched on when a vehicle is

covering the section thus preventing pedestrians from being injured. This system is

used in Bordeaux with trams and is called Alimentation par Solution. The French

Wikipedia article states that it is three times more expensive than the Catenaries

system.

Fig. 8.1 dual mode transit monorail

Cities with slow air exchange (inversion) and high emission figures (particulate

matter PM10, PM2.5, NOx, Ozone) caused by diesel-powered vehicles, need a way to

reduce big pollution sources. Commercial diesel-fueled vehicles are prime targets

because of their high NOx and PM emissions caused by the lack of sufficient

pollution controls. This is the main motivational idea for development of this

technology.
http://en.wikipedia.org/wiki/Mass_transithttp://en.wikipedia.org/wiki/Mass_transithttp://en.wikipedia.org/wiki/Rubber_tyred_tramhttp://en.wikipedia.org/wiki/Guided_bushttp://en.wikipedia.org/wiki/Pedestrianhttp://en.wikipedia.org/wiki/Bordeauxhttp://en.wikipedia.org/wiki/Tramhttp://en.wikipedia.org/wiki/Catenary_%28railways%29http://en.wikipedia.org/wiki/Particulate_matterhttp://en.wikipedia.org/wiki/Particulate_matterhttp://en.wikipedia.org/wiki/Particulate_matterhttp://en.wikipedia.org/wiki/Particulate_matterhttp://en.wikipedia.org/wiki/Catenary_%28railways%29http://en.wikipedia.org/wiki/Tramhttp://en.wikipedia.org/wiki/Bordeauxhttp://en.wikipedia.org/wiki/Pedestrianhttp://en.wikipedia.org/wiki/Guided_bushttp://en.wikipedia.org/wiki/Rubber_tyred_tramhttp://en.wikipedia.org/wiki/Mass_transithttp://en.wikipedia.org/wiki/Mass_transit


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8.2. AUTOMATED HIGHWAY SYSTEMS

Fig.8.2 AUTOMATED HIGHWAY SYSTEMS

An automated highway system (AHS) or Smart Road is a proposed intelligent

transportation system technology designed to provide for driverless cars on specific

rights-of-way. It is most often touted as a means of traffic congestion relief, as it

would drastically reduce following distances and headway, thus allowing more cars

to occupy a given stretch of road.

In one scheme, the roadway has magnetized stainless-steel spikes driven one

meter apart in its center. The car senses the spikes to measure its speed and locate

the center of the lane. Furthermore, the spikes can have either magnetic north ormagnetic south facing up. The roadway thus has small amounts of digital data

describing interchanges, recommended speeds, etc. The cars have power steering

and automatic speed controls, which are controlled by a computer. The cars

organize themselves into platoons of eight to twenty-five cars. The platoons drive

themselves a meter apart, so that air resistance is minimized. The distance between

platoons is the conventional braking distance. If anything goes wrong, the maximum

number of harmed cars should be one platoon.
http://en.wikipedia.org/wiki/Intelligent_transportation_systemhttp://en.wikipedia.org/wiki/Intelligent_transportation_systemhttp://en.wikipedia.org/wiki/Driverless_carhttp://en.wikipedia.org/wiki/Traffic_congestionhttp://en.wikipedia.org/wiki/Headwayhttp://en.wikipedia.org/wiki/Lanehttp://en.wikipedia.org/wiki/Power_steeringhttp://en.wikipedia.org/wiki/Platoon_%28automobile%29http://en.wikipedia.org/wiki/Braking_distancehttp://en.wikipedia.org/wiki/Braking_distancehttp://en.wikipedia.org/wiki/Platoon_%28automobile%29http://en.wikipedia.org/wiki/Power_steeringhttp://en.wikipedia.org/wiki/Lanehttp://en.wikipedia.org/wiki/Headwayhttp://en.wikipedia.org/wiki/Traffic_congestionhttp://en.wikipedia.org/wiki/Driverless_carhttp://en.wikipedia.org/wiki/Intelligent_transportation_systemhttp://en.wikipedia.org/wiki/Intelligent_transportation_system


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9. IN MOVIES

As we can see that virtual driver system provides very high technology vehicles, this

also attracts the film industries towards adding it in the various science fiction movies

or any type of movie related to science. Automatic vehicles have been introduced in

cinema in 1989 by a famous movie Batman.

After this these cars are used in 1994 for the film Time cop, in 2000 forThe

6th Dayand in 2004 for the film I, Robotespecially for driving and parking.

Not only Hollywood, these cars also attract our Bollywood cinema. The 1st

automatic car is used in 2004 for Tarzan -The Wonder Car.


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10. CONCLUSION

A method for decision making in collision avoidance applications has been

presented. The main advantages of the method are:

The use of modern tracking theory makes it straight forward how to deal with

measurement and process noise

Motion in two dimensions is considered.

The prototype system presented in this paper significantly reduces the impact speed

in frontal collisions. As can be seen in figure 17 interventions typically occur when

the obstacle is closer than 20 m away from the CMBB vehicle (more than 90 % of all

rear end collisions occur at relative speeds below 100 km/h. A sensor with a shorter

detection range but a larger field of view might be more appropriate for collision

mitigation purposes. Further work on the sensors and the sensor fusion is needed to

have a system with 0 faulty interventions. It would be desirable to have a sensor with

better target classification capability.

A revolutionary change has been occurred in the driving style. As we are going

searching and searching new technology we become a member of advance society.As we found new technology for driving a carin easy way, which made our life easier

that finally means: -

Technology - The spice of Life.


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11. REFERENCES

1. Decision Making for Collision Avoidance Systems by Jonas Jonson, Jonas

Johansson.

2. www.wikipedia.org

3. www.howstuffworks.com

4. www.nhtsa.gov/people/injury/research/pub/acas/ACAS_index.htm

5. www.tracks.com
http://www.wikipedia.org/http://www.wikipedia.org/http://www.howstuffworks.com/http://www.howstuffworks.com/http://www.nhtsa.gov/people/injury/research/pub/acas/ACAS_index.htmhttp://www.nhtsa.gov/people/injury/research/pub/acas/ACAS_index.htmhttp://www.tracks.com/http://www.tracks.com/http://www.tracks.com/http://www.nhtsa.gov/people/injury/research/pub/acas/ACAS_index.htmhttp://www.howstuffworks.com/http://www.wikipedia.org/

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Virtual Driver System Full Report