ATLAS Design Phase

ATLAS
Prepared by – Cedric Melin s3347116
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ATLAS
Advanced Traffic Light Approach System
-
Design Phase

ATLAS
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Table of Contents
Table of Contents ................................................................................................................2
Executive summary ..............................................................................................................3
1 Background.................................................................................................................4
2 User profile and requirements ............................................................................................5
3 Current market, Opportunities to excel and Customers ...............................................................6
4 Objectives tree .............................................................................................................7
5 Statement of Requirement ................................................................................................8
6 House of Quality.........................................................................................................10
7 Concept Generation and Selection.....................................................................................12
8 Detail design .............................................................................................................18
9 Conclusion................................................................................................................24
10 Index....................................................................................................................25
11 Reference...............................................................................................................25

ATLAS
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Executive summary
Problem – On-road observations of multiples driver
behaviours showed that the most common driving
mode is brake and race from traffic light to traffic
light. Traffic light time windows are programmed by
a control centre therefore less aggressive driving
mode performs equally well. In other word the Hare
does not win the race, the Turtle either; both arrive at
the same time at the same location (next traffic light).
However, the Turtle spent less energy and took less
risk. How can the Hare become a Turtle?
Method – A statement of requirement (SOR) was
established based on driver interviews and extensive
research on traffic light encroachments, and driving
assistance technology. Both methods led to uncover
excitement needs and to concomitantly design for
drivers and car manufacturers. The user interviews
did not pin point towards the classical Pareto
histogram response. Therefore, the statement of
requirement is open ended and none specific.
Subsequently, a house of quality was populated using
drivers basic and performance needs against technical
requirements most of which were customer tests.
Since users needs are not clear (unspoken voice),
each objectives were ranked subjectively against the
customer interview and the state of the art in research
and driving assistance technology. Once, basic and
performance needs were weighted out; concepts were
generated using a biologic model and a
morphological analysis. The various systems
generated were assessed using a weighted decision
matrix.
Findings – Based on the users interviews, it was
found that 91% wanted information regarding to
traffic light time windows, 85% wanted to decrease
Co2 emission, 74% were favourable to technology
that address fuel consumption while only 21% were
favourable to technology that manage the car
velocity. When drivers were asked why, the most
common answer was: I want to be in control.
Consequently, the SOR affinities were formulated as
followed safety, comfort, pollution, cost, and user
friendliness; and weighted out in the Quality
Function Deployment (QFD) 4, 3, 5, 5, 2, 3, and 5
respectively on a scale from 1 to 5. The QFD showed
that three technical requirements (TR) had to perform
extremely well (basic needs). Those TR were CT5
(customer test 5 addressing customer satisfaction),
CT3 (addressing psychological comfort), and Co2
emission.
Solution – The SOR had for goal: ‘Intelligent
management of traffic light time window to improve
driving experience and Co2 reduction’. The solution
derived from concept generation and the WDM
iterative process is known as ATLAS (Advanced
Traffic Light Approach System). It processes a
digital signal that includes the traffic light average
velocity and the road gradient of the upcoming traffic
light time window in relation to the vehicle location
and speed. ATLAS converts the digital signal into
required energy output for a specific traffic light time
window (TLTW). Three different driving modes
have been retained to cater for the driver preferences,
traffic conditions, and traffic complexity. The target
customer for ATLAS technology was Volvo; the cost
to develop such technology was controlled by using
existing technologies.

ATLAS
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1 Background
Traffic lights are visual signalling devices that were
first used in London in 1868 (Wikipedia). The colour
code indicates the driver when to proceed with
entering the intersection or when to stop. In 1936 the
Marshalite integrated time to the colour code. They
were removed from Melbourne’s traffic light system
in the 1970s (Jess3). In relation to traffic light, Kent,
B extrapolated an occurrence of 500,000 dangerous
encroachments per year at traffic signal. This study
indicates that the Marshalite may have provided the
driver with critical information fogging its judgement
and compromising safety. Despite the lack of
information and decision on behalf of the driver, law
infringements at red light are common and the object
of intense law enforcement method using camera
surveillance. Territo, C. Showed that red light
running violation has decreased on average of 55
precents at all location in the city of St Louis, MO
USA. Despite, law enforcement, the prevalence of
traffic red light infraction remains a reality that
underpins both opportunistic driver behaviour and
poor traffic light system design. The Marshalite tried
to improve traffic light design by informing the
driver of the imminence of red light. In fact, Kent, B.
showed that 93% of the encroachments occurred
during the all-red period of the signal cycle when the
probability of conflicting traffic is the lowest. While
driving, if the green light goes, the driver must makes
a judgment call integrating vehicle velocity, distance
and time to either break, accelerate or maintaining its
speed. Such decision and associated consequences
are stressful driving experiences. On road driving
observations (see schematic below) indicate various
driving strategies to deal with traffic light time
windows. Two driving modes to manage traffic light
time window exist: to speed limit (grey) and to traffic
light average speed (green). Traffic light average
velocity means that no matter how hard the
accelerator or braking pedals are stressed, we all
arrive at the same time to the next traffic lights. The
underpinning urban driving concept is time
performance. From driving observations, it seems
that this concept is poorly applied by modern drivers
despite numerous advantages such as lower energy
consumptions; lower running cost (brake pads, tires);
greater safety (lower speed) and more relax driving
behaviour. It is important to note that since time is
unknown, hence it is impossible for driver to find and
to adjust to the traffic light optimum average
velocity. Adding to this concept, if energy is wasted
due to a lack of information with regard to time, the
road topography plays a major role in efficiently
managing time against energy usage. The following
analysis aim is to produce a system to manage traffic
light intervals to provide drivers with a better driving
experience and lower Co2 emission.

ATLAS
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2 User profile and requirements
Costa, G. showed that driving is a stressful task that
requires high levels of attention and vigilance. Both
quality are intertwined and influenced by daily life
factors (working hours, weather, fatigue, passengers,
and so forth ...). Cumulated to the impact of daily life
factors on accident probability, the aging
demography of the developed world will drastically
affect the bulk of the driver population driving
capabilities. Susillowati, H. showed that older drivers
have a longer response time to set driving test
compare to younger driver. Susillowati, H. also
concluded that older driver will feel safer in cars with
advanced driving assistance technology which may in
return adversely affect their attention and vigilance to
hazardous road condition. Such research pin points
latent customer needs and requirements beyond their
awareness. Therefore ATLAS must avoid the
paradox of engineering safe car that boost driver
confidence in unsafe zone, especially at nevralgic
points such as traffic light. ATLAS must aim to
satisfy both safety features and quality urban
driving while avoiding the ‘Transformer effect’. To
assess ‘The Transformer Effect paradox’ a survey
was conducted. The driver interview (see figure 1-
Left) shows that drivers unsurprisingly associate
traffic light with hazards (stressor). The most
critical finding is that drivers did not want
technology to regulate vehicle speed to traffic light
condition which corroborates well with the
importance of the stressor. Discussing the
underlying reason, eighty six percents of
respondents associated the idea with potential
decision making conflicts. This was especially
prevalent within male drivers (qualitative
appreciation). However, drivers were highly
positive to the idea that the car could manage fuel
consumption to driving conditions. Finally,
information concerning the status of traffic light was
highly regarded by drivers. This also corroborates
with the stressor. Indeed, intuitively the driver needs
to forecast his/her actions in relation to increasing
risk at traffic lights. To conclude, car users have
latent needs that conflict with their primitive instinct
for survival. To summarize user requirements are:
 Information with regard to traffic light is
important.
 Carbon dioxide reduction is highly
regarded.
 Controlling fuel consumption is highly
regarded.
 The driver must feel in control.
 The driver will not delegate any
responsibility to technology.
Figure 1 - ATLAS preliminary survey
0
5
10
15
20
25
30
35
N.ofrespondent
Yes
No

ATLAS
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3 Current market, Opportunities to excel and Customers
In the western world, urbanisation has reached 80%
of the local population (Wikipedia). Worldwide, the
urban population has overtaken rural population.
Some car manufacturers such as Volvo have clearly
identified the need for driving assistance technology
(DAT) to improve car safety features (see picture 2,
3, 4, 5) in urban environment. A second category of
DAT is engineered for highway such as the
Mercedes-Benz ML 350 which has 4 safety features
(pre-braking, distronic high way radar, active lane
keeping assistance, and active blend spot assist).
Both category of DAT give critical information to the
driver as picture 2, 3 and 4 shows. More advanced
safety features take control of the braking system
such as picture 5 below (Volvo pedestrian and cyclist
detection system). Despite the introduction of safety
features ranging from information to vehicle control,
in most case the driver remains the decision maker.
The Google driverless car (picture 6 below) has been
amended by only 3 states in the USA (Wikipedia)
which point towards a difficult path towards fully
automated vehicles in the near future for both law
and cost reasons. Despite the great potential of
automated car for both passenger and car-pooling
(new age public transport); it is prudent to conclude
that for the foreseeable future, driver assisted
technologies will continue to enhance car safety
system around the driver. ATLAS will aim to
develop on-board Artificial Intelligence to work with
the driver to enhance driving experience and reduce
Co2 emission. The potential customer is Volvo for
their vision to design around drivers (7). It is also
very clear that they dominate the current urban DAT
landscape which is relatively empty. By keeping their
development pace, Volvo can increase its brand value
for future customers looking for new car functions
(driverless car). Two objectives have been identified
from video presentation of their technology and
website.
 User friendliness
 Cost
ATLAS relies on the assumption that road traffic
authorities will broadcast traffic signal schedules
under a set of legal constraints and on-road
prototypes.
+Law
+ CostMature Market Market
Node
6.
1.
2. 3.
4. 5.
AI
7.

ATLAS
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4 Objectives tree
Following the analysis of users and customer
requirements, the objective tree below integrates user
requirements into three main components (safety,
comfort, Co2 reduction) and the customer (Volvo)
two main objectives (cost and user friendliness).
Each objective is assessed against a series of customs
test or more tangible metrics (gr Co2/km). Four
customer tests have been engineered to assess how
the user interacts with car onboard technology and its
perception of comfort in relation to traffic lights,
those will be explained later on. Carbon dioxide
emission in relation to traffic lights is quantified
using grams of Co2 per kilometre (how much energy
is required to go through a set of traffic lights). While
analysing online Volvo road assistance technology
presentation video (Volvo website), it was clear that
Volvo emphasized on how driving assistance
technology behave with regard to driver expectation.
For that simple reason, a custom test was design
(CT5). Finally, since ATLAS as no precedent, the
objective remained opened rather than specific to
leave more room for creativity and on-going
improvement at different stage of ATLAS
development.
User
Intelligent
management of
traffic light time
windows to
improve driving
experience and
Co2 emission
Safety
Comfort
Environmentally friendly gr Co2 /km
CT 1Driver attention
Passive safety CT 2
CT 3Sense of control
Less work CT 4
Cost % of new components
User friendly CT5
Complies with road traffic act
Customer
Constraints

ATLAS
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5 Statement of Requirement
ATLAS is a revolutionary product. It is not on the
market (no available precedent) and the design of
ATLAS is entirely new (to current research) in the
interaction and output of known technologies (system
elements).
Customer Test 1 -
Test background: Susillowati, H has concluded that
driver may lose driving attention due to advanced
safety features.
Test: Measure driver attention in driving condition.
Test method: Compare the frequency of hazardous
situations for high tech safety cars vs. low tech safety
cars. (Equip both groups with radar technology)
Test outcome:
- % of hazardous situations HTC
- % of hazardous situations LTC
- Test for statistical significance
Customer Test 2 –
Test background: At slower speed the stopping
distance to avoid a hazard increases improving
safety.
Test: Measure urban collision rate at different speed.
Test method: A representative sample of the driver
population is tested on racing circuit. The test
consists of introducing hazard while they are driving
within an artificial urban setting to assess their
avoidance capabilities under various speed limits.
The subjects are told that we are carrying road noise
research to limit interference with their natural
behaviours.
Test outcome:
- % of collision at 70 km/h
Test for statistical significance
Customer Test 3 –
Test background: When submitted to an
environmental stressor (eminent accident), the body
physiologic respond to stress by increasing heart rate
and direct blood flow to critical part of the body
Johnson, MJ.
Test: Measure driver heart rate in driving condition.
Test method: Measuring blood heart rate frequency is
made possible by video technology developed by
Hardesty, L MIT.
Test outcome: Determine what is perceived as unsafe
/ safe by the driver.
- % of driver Heart rate superior to mean heart
rate (60 pulse / min) under acceleration at
traffic light
- % of driver Heart rate superior to mean rate
(60 pulse / min) under deceleration at traffic
light
- Test for statistical significance
Customer Test 4 –
Test background: Driving on road with a high density
of traffic lights requires constant work input from the
driver to actuate pedals to suit the local traffic
condition. Constant work may negatively affect
driver behaviour.
Test: Measure change in driver work input under
various urban traffic light conditions.
Test method: Set a speed track with traffic lights. Set
a car with accelerometers to register the amplitude of
stop and start. Test a representative sample of the
driver population to drive for an hour (average travel
time). Test three different setting (high, medium and
low frequency red light. The subjects are told that we
are carrying road noise research to limit interference
with their natural behaviours.
Test outcome per red light frequency:
- % of hard braking / time slot
- % of hard acceleration / time slot
- % of driver swearing
- % of driver not satisfied of their drive
Test for statistical significance
Customer Test 5 –
Test background: The voice of the customer is the
drivers design input (Leary, M). However, certain
customer needs are not obvious to the customer and
are difficult to pin point with questionnaires or
observations especially with regards to revolutionary
product. Therefore, the customer/user shall interact
with the product/prototype to clearly determine
unforseen features by the designer.
Test: Measure driver satisfaction under different
ATLAS urban driving.
Test method: Use a simulator to analyse the reaction
of the driver as he/she drives.
Test outcome:
- Identify features that are not suited
- Identify features that must be improved
- Ask users feedback
- Redesign and test until satisfaction is cost-
effective

ATLAS
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RTA
Road traffic acts
User/Driver
Constraints
Up
Unit of measurement Preferred direction
Volvo
Goal – Intelligent management of traffic light time windows to improve driving experience and Co2
reduction
Up
Up
Down
Up
Down
Down
CT1
CT2
gr of Co2/km
CT3
CT4
Sense of control
Less work
Cost
User friendly
Objectives
Driver attention
Passive safety
Environmentally Friendly
% of new components
CT5
Table 1 - Statement of Requirements

ATLAS
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6 House of Quality
a) CR-TR correlation
i) CR1. and TR1.
Active safety with regard to traffic light time
windows is satisfied by maintaining driver attention
to traffic condition. This customer requires is
‘hidden’ as described by Susillowati, H. ATLAS aim
is not to substitute driving but to enhance traffic light
approach. The driver remains the decision maker
with regard to breaking and stopping. Therefore the
CR-TR correlation is strong.
ii) CR2. and TR2.
Passive safety with regard to traffic light is satisfied
under the hypothesis that the average speed under
ATLAS management is decreased compared to
conventional driving. By consequence, the time to
stop the vehicle in presence of hazard is decreased.
Therefore the correlation is strong.
iii) CR 3. and TR 3.
To reduce Co2 emission with regard to traffic light
time window, the technical requirement to achieve
this customer requirement is to use ‘free’ energy
from the vehicle momentum. The less energy used
the less Co2 emitted. Therefore the CR-TR
correlation is strong and measured in gram of Co2
per kilometres.
iv) CR 4. and TR 4.
Driver comfort in relation to traffic light time
window is linked to stressor (red light associated
risk). The TR to achieve CR2 is based on a customer
test to assess their response to acceleration or
deceleration. Hence, it is assumed that driver
psychological comfort will increase if ATLAS
responds to traffic lights only using deceleration.
Therefore the CR-TR correlation is strong.
v) CR5. and TR5.
It is assumed from personal experience that not
having to stop at traffic light is always welcome. It
requires less work input (ei – hill start). Therefore,
passing a green light is more comfortable than
stopping at red light. Therefore the CR-TR
correlation is strong.
vi) CR6. and TR6.
There is a limit to what customers are ready to pay
for safety. However, an effective usage of current
technology may limit the percentage of new parts
required. Therefore the CR-TR correlation is strong.
vi) CR7. and TR7.
The voice of the customer is primordial for
successfully gain market shares. However, the
planning phase of revolutionary product may include
limited customer satisfaction input until prototypes
are ready to be tested by a wider audience. Therefore
the CR-TR correlation is strong.
b) TR-TR correlations -
i) TR1-TR2 and TR5 strong negative correlation
Why – If ATLAS slow done has it approach traffic
light going red, it is probable that the driver will get
use to a different style of driving where it relies on
ATLAS to manage risk. Therefore, driver attention
may decrease with time has it trust ATLAS.
Opportunity – Develop a close loop system that
ensures driver attention.
ii) TR2-TR3 strong positive correlation
Why – When ATLAS modulates energy consumption
to traffic light time window, for the same ‘time
performances’ it significantly increase the time to
avoid hazards hence decreasing collision probability.
Opportunity –Use the concept of ‘time performance’
to develop marketing strategy.
iii) TR6-TR7 strong positive correlation
Why – Customer satisfaction may require new
components that were not foreseen during the
development phase.
Opportunity – An extensive use of simulator to test
users’ response to ATLAS may provide greater
insight to the chef designer.

ATLAS
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TR direction up Down Down up Down Down up
Customer Requirements (CR) CR rating
(1) Driving attention 4 9
(2) Passive safety 3 9
(3) Environmentaly friendly 5 9
(4) Sense of control 5 9
(5) Less work 2 9
(6) cost 3 9
(7) User friendly 5 9
36 27 45 45 18 27 45
Driver
(6)%ofnew
components
Volvo
(7)CT5
TR Importance
Technical
Requirements(TR)
(3)(grofCo2/km)
(1)CT1
(5)CT4
(2)CT2
(4)CT3
c) ATLAS quality function deployment
d) ATLAS QFD synthesis
The QFD above clearly shows that to seduce users
ATLAS must perform extremely well with regard to
Co2 emission and user’s perception of ‘keeping
control of their vehicles’. It also shows that Volvo
must reach high customer satisfaction prior to
releasing their product. If this QFD can guide the
design team to avoid the above pitfall, it does not
clearly shows the interaction between ATLAS and
the users; hence the map road to success is unclear.
Indeed, it is critical to understand that the protocol
through which ATLAS is activate or deactivated
under complex urban driving will heavily influence
customer satisfaction. Indeed ATLAS must not
frustrate the users but assist the making decision
process. Since such product has no precedent, it is
not a kaizen matter but a matter of psychological
analysis to set user’s need within clear design
boundary conditions.

ATLAS
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7 Concept Generation and Selection
a) Traffic light analogous system - Concept philosophy
Traffic light system Blood circulation system
System schematic System schematic
Manmade systems - 130 years of evolution Life - 1 billion years of evolution
Interpretation –
Traffic light system are open loop system which is
not equiped for feedback. The comparaison between
the traffic light system and the physiology of the
heart rate is based upon common features such as
centralisation (Traffic light control centre vs. brain)
and an equally complex circulation system that
maintain all area of the body / economy alive by
providing (good/money/oxygen/sugar/protein) to
cells (business / neurones). Upon such observation, a
simple question arose: Why is it so that the light
always gives orders? Indeed in physiology, local
needs are communicated to the brain which adjust
blood flow according to demand. If you start to eat,
blood flows to the intestin. If you start running, blood
flows to your lungs and legs. Therefore, why does the
traffic light does not communicates to the vehicle so
that the receiver (vehicle / driver) can adjust to higher
needs. If the brain recognises a threat, the blood flow
is reoriented, regardless of local needs (intestines)
hence digestion is slowed via limiting enzymes
secretions. Similarly, traffic condition dictates time
envelopes that regulates traffic following higher
priorities than local needs (me). However,
information shall be dispatched to make local
changes more efficient (conservation of energy). If
their is one things that physiology does very well, it
is to conserve energy to survive. From this
interpretation of physiology, the regulation
mechanism that efficiently modulate organs response
to brain orders will be applied to vehicle/driver in
relation to traffic light.
System 1
Traffic
Light
System 2
Driver/car
System 1
Brain
System 2
Heart/artery

ATLAS
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Courtesy:Wikipedia
b) Conception generation for systems – Using the
DNA model
Conception generation using symbol association is
inspired from DNA where 4 basis (see picture
right courtesy Wikipedia) sequential
arrangement (combination) produce complex
and unique organisms extremely well
‘engineered’ to their environments. The lesson to
be learnt from DNA is that the elements are
known and irrelevant as such; yet their
sequential arrangement is what drove evolution to
successfully survive on earth. Therefore, the
aim is to find basic elements within a system
and to investigate potential interaction to find
the current best possible combination. A matrix
approach to brainstorm multiple combinations
of elements is possible. For the scope of this
assignment only selected elements related to
the SOR have been brainstormed.
Symbols system elements brainstorm –
ECU Propulsion Brakes Seat
Seat
belt
Gyroscope KERS
Steering
wheel Gearbox Rotation
Gear stick Pedal Antenna
Force Emotion
Camera
Traffic
light
Speedo
Torque Stress
Pleasure Audio Haptic
Visual Smell
Time Close loop
Open
loop Object
Road
Distance KE PE
Work
Slope
Day Glare Night
Rain $
Co2
HUD Cartesian Death
GPS
Wiper
Memory Acceleration Deceleration
Radar
Momentum

ATLAS
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c) System morphological analysis to generate concepts–
System 1 - System 2 –
System 3 –
$
System 4 –
$
System 5 – System 6 –

ATLAS
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d ) Concepts selection using Weighted Decision Matrix (WDM) –Weighting factors (prior Knowledge)
The following weighting factors have been selected
in relation to their relative importance and the need to
justify design choice.
i) Safety and traffic Light status–
Giving critical information concerning the traffic
light status to the driver is deemed risky and unsafe.
Indeed, knowing that the light will shortly turn red
may be wrongly used in relation to the power of the
car, the driver priorities, and simply ‘Plank’ games.
No safety data regarding the use of Marshalite was
found. However, no car manufacturer will warrant
their technology to increase road accident
probabilities. In the WDM such system was assumed
to boost driver attention (positive active safety),
however the consequence of such action was negated
under passive safety.
ii) Signal path to driver –
Scott, J.J showed that tactile warning systems to alert
the driver of eminent rear-end collision have the
shortest RT compared to visual and auditory in
simulated condition. However, Kramer, AF found
that combined audio/visual CAS (collision avoidance
system) in simulated conditions yield the best
performances with a significant benefit for older
driver (60-82 years of age). Johnson, MJ noted that
the physiological response of is subjects under
simulation and real world experiences remains
different which indicates that simulated results must
be cautiously interpreted. In the case of ATLAS, the
information given to the driver is not related to an
imminent crash but to a ‘driving mode’ to manage
the traffic light time envelop. ATLAS remains a
lower priority information path than the warning
from crash avoidance system. However, since
ATLAS modulates the driving mode at a nevralgic
points (traffic light approach) the driver attention and
readiness must remain high. For this reason, the
haptic system is preferred over heads-up and audio
system which maybe use for CAS.
iii) Customer Test 3 –
It evaluates the response of drivers to the automatic
acceleration and deceleration of the vehicles
approaching traffic lights. It is assume that
deceleration is associated with safety and is a haptic
signal path (felt by the entire body). Acceleration of a
vehicle is assumed to be associated with loss of
control hence discomfort.
iv) Customer Test 2 –
This CT associates the driver’s safety perception to
effective change of driving behaviour. If ATLAS
improves comfort and Co2 reduction therefore Safety
is a by-product and won’t be perceived has a safety
features.
v) Co2 emission and energy consumption –
It is assumed that the least energy is consumed, the
least Co2 is emitted the concept is also valid for
electric car except that it increases their range. The
optimum is to avoid idling time at traffic lights.
Energy expanded to break and accelerate has
negative impact on Co2 emission.
vi) User satisfaction
User satisfaction is weighted against the user survey.
The survey clearly showed that the user will sanction
any system that takes control away from their driving
options. Therefore, the weighting factor (9) considers
that the driver would be comfortable to delegate
responsibilities to ATLAS. However, weighting a
WDM against a survey or a perception of what the
customer want has an associated failure risk.
Therefore, designer must bear in mind that ATLAS
may follow two independent development paths:
 ATLAS hardware
 ATLAS behaviour using on-going customer
test either via prototype or via simulator.

ATLAS
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System 1 System 2 System 3 System 4 System 5 System 6
Active safety 4 9 9 1 3 9 3
Passive safety 3 -9 3 3 3 1 1
Co2 emission 5 1 1 9 9 1 1
Psychological comfort 5 9 3 1 9 3 3
Physical comfort 2 1 3 3 3 1 1
Cost 3 9 3 3 3 1 3
User friendly 5 9 3 3 9 9 1
133 95 93 171 109 51
0.55 0.39 0.38 0.70 0.45 0.21
Table 2 - Weighted Decision Matrix (WDM)
Concept
CRImportance
Sum to full potential
Sum
UserVolvo
vii) Weighted Decision Matrix
The WDM below was weighted out using the above
knowledge and assumptions. It quantifies each
system against ATLAS objectives into two sums:
 Raw sum multiplying objective value by the
given rating.
 Sum to Full Potential (SFP) is the raw sum
divided by the sum of objectives multiplied
by nine (maximum rating)
SFP is particularly important in term of system
analysis, as it pin point the level of imperfection for a
given system against a theoretical benchmark. The
WDM below does not pin point to a particular silver
bullet system solution. However, it shows that the
current concepts are far from the full ATLAS
potential. The most obvious solution to reach
customer requirements is to utilise System four as a
base to incorporate other systems strengths.

ATLAS
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e) Patent search
A patent search uncovered the following United
States patent. Its main function is to notify the car
driver of the traffic signal status. An analysis of the
patent claims has yielded a number of options to by-
pass the patent without infringing it. The main
avoidance strategy is to communicate the status of
the traffic light to the vehicles hence by-passing the
driver has prime information recipient. The patent
doesn’t account for such function as it can be seen
below, hence it is not infringed. Furthermore, it fits
within ATLAS WDM safety criterion; giving critical
information directly to the driver may create a
significant risk which may attract criticism from a
legislative point of view and leading to abort
ATLAS. Therefore the solution to inform the driver
of the traffic light status is eliminated from further
system engineering for two reasons:
 it would infringe Patent 20130063281
 It may attract negative legislative outcome
with regard to safety.
SS1 – Patent Application 20130063281- Source – http://www.faqs.org/patents/app/20130063281
Claims – Patent Function Avoidance – New function
Avoided function – Circuitry configured to receive
information associated with vehicle traffic signal status from
the vehicle traffic signal controller....
New function – The vehicle traffic signal broadcast an ID
number to the vehicle. The vehicle traffic signal controller
broadcast the time envelop of the ID number to the vehicles.
Avoided function –The vehicle traffic signal notification ...
signal to the user ...
New function – The vehicle traffic signal notification ...
signal to the vehicle ECU/PCM to modulate its speed ... The
ECU/PCM signal the driver that speed control is over ...
Avoided function – ... from the group consisting of a radio
frequency signal, an ultrasonic frequency signal, and a
microwave frequency signal ...
New function - ... Use Infrared frequency signal ...

ATLAS
Page 18 of 25
8 Detail design
a) ATLAS system –
Following the WDM results and patent search the
following ATLAS design has been iterated. In
relation to user objectives, system elements have
been combined into three sets of variables (see figure
1 below). Set one address the traffic time window; set
two address energy consumption and set three
address adaptive features.
b) ATLAS block diagram –
1
Figure 2 -
2
4
6
5
GPS TLM
Set point
desired
energy
Input
Traffic sensors
+
+
Energy
Output
Driver sensors
- -
93
Drive train
-
7
8
Traffic sensors
+ 10
11
Figure 1 -
Set 1
Set 2 Set 3

ATLAS
Page 19 of 25
c) Embodiments (block diagram) –
1. ATLAS is activated by the driver which decides of
the level of energy saving, comfort, safety and
driving modes. A visual signal on the dash-board
indicates the driver that ATLAS is engaged.
2. Once ATLAS is engaged, the accelerator becomes
the primary setting point. There are three different
driving modes available to the driver. Each are
clearly explained using flow charts (see page 20-22).
Regardless of the driving mode ATLAS reaches the
desired energy input with regard to traffic light time
windows, road gradient and traffic condition. The
role of having various driving mode is to suit
personal preference, traffic condition and driving
complexity.
3. Traffic lights time windows are transmitted to the
car via GPS traffic light map (TLM). Each light has
an ID cross referenced with the GPS TLM. The GPS
TLM sends the vehicles the average velocity to reach
the next red traffic light (RTL).The average velocity
is solved for net energy equals to zero (most
optimum setting) at red lights. It includes the kinetic
energy of the vehicle and the potential energy ahead
til the forecasted RTL. Forecasted potential energy is
source from a gradient map (datum is the sea level)
overlapping the GPS TLM.
4. Traffic sensors adjust the final vehicle velocity
with regards to rear and front traffic. When the
vehicles start to decelerates, traffic sensors (radar)
monitor the rear and front distance in relation to
neighbouring vehicles. If the rear vehicle is deemed
too close (safe breaking distance), the vehicle
decreases the rate of deceleration. Frontal radars
remain dedicated to CAS.
5. The vehicle on-board computer receives the
average velocity converts it into engine power output,
KERS power intake, or coasting. The on-board
computer controls the ECU (6); the EC either
maintains vehicle speed or decelerates. Acceleration
to increase velocity is prohibited. The ECU controls
propulsion (fuel pump or electrical controller) and
KERS (7) (8).
9. Driver sensors are closed loop haptic path that
amplify deceleration in case that the driver is not
responding to upcoming RTL. A warning close loop
alert the driver that ATLAS has finished to manage
the time envelope. If the driver does not respond
(apply breaks) KERS is switch on to increase the
current rate of deceleration. The close loop consist of
an haptic signal (stirring wheel vibrations similar to
iPhone to limit visual information overdose), if the
driver does not respond under a set time by applying
pressure on the breaking pedal, KERS intakes more
energy. When KERS increase the rate of
deceleration, the driver entire body is subject the
signal which cannot be disregarded. The rate of
deceleration shall be marked enough to alert the
driver but it shall not endanger the rear vehicle.
10. If the haptic close loop is activated, the rear
traffic sensors (rear radars) modulate the rate of
deceleration to maintain safe distance.
11. ATLAS’s flow charts see below flow chart 1 to 6.

ATLAS
Page 20 of 25
d) Embodiments (Flow charts)
Figure 3 - Flow charts terminology

ATLAS
Page 21 of 25
d) Embodiments (Flow charts) continued …

ATLAS
Page 22 of 25
d) Embodiments (Flow charts) continued …

ATLAS
Page 23 of 25
e) Cost strategy and Manufacturing
The cost strategy adopted answers the following
question – How can we provide our customer with
more features using the same technology platform?
In other word can return on investment be improved?
Return on investment is the net profit over
investment. In the case of ATLAS, a new feature is
provided to car user to improve quality of urban
driving, carbon dioxide reduction (energy
consumption) and safety which shall increase sale or
market share. To satisfy those objectives, existing
components are modified while very few new and
expansive components are required.
ATLAS
components
Changes Custom vs. commodity
ATLAS interface Touch screen and voice recognition dashboard to
choose ATLAS features (level of comfort, level of
energy saving, multiple driver settings, destination
etc...). Such dashboard has already been designed by
Volvo (caradvice.com).
Commodity – This technology is borrowed
from the mobile phone industry and currently
prototyped by Tesla (CNet.com).
Custom – The dash board connection must be
redesigned to suit touch screen technology.
Perhaps a design on the stirring wheel could
make it easier access to set up ATLAS while
the car is warming up, or at stand still.
On-board GPS None, already existing and use as part of a 5 year
warranty planned (Volvocar.com).
Commodity–The GPS market was forecasted
to grow from US$ 3 billion to US$ 6-8 billion
from 2008 to 2012 with a majority of market
share hold by Garmin and Tom Tom’s hence
GPS remains a commodity.
Accelerator pedal
and cruise control
actuator for mode
one and two
None, this technology is already used for adaptive
cruise control. GPS TLM is the input signal while the
car is under acceleration. Current cost € 2250
(Volvocar.com).
Commodity– Since this product is used on a
large scale, it is reasonable to assume that it is a
commodity part. Volvo’s duties are to test for
failure probability using stringent tests.
Accelerator pedal
for mode 3
Redesign is required, the system does not exist as of
yet. The idea is that an actuator could alternatively
switch an input knob from the accelerator to brakes
function and vice versa. Design cost and safety
constraint may render the design difficult hence
expansive.
Custom – Since it is a critical component, and
a unique idea that maybe patented, hence Volvo
shall make it a custom part. The conceptual
phase can be contracted to specialised design
bureau to fast track production.
On-board hardware Hardware to process GPS TLM signal is required Commodity –since it is an electronic matter,
this may not be Volvo specialty; hence it is
most time and cost efficient to find highly
competent partners.

ATLAS
Page 24 of 25
ATLAS
components
Changes Custom vs. commodity
ATLAS Software The software does not currently exist. Such
development maybe expansive, however to judge by
the new Mercedes-Benz ML 350 model and Volvos
safety features, it is reasonable to assume that
Artificial Intelligence is rapidly becoming the 21st
century added function to cars. This is paving the
way to fully driverless technology such as the Google
car. It seems very obvious that Google could provide
virtual maps (signalisation and gradient) while car
manufacturers will focus on delivering highly reliable
technology. Therefore such cost are associated to
research and development which are overheads.
Custom – Since critical hardware (CPU, KERS
etc...) are activated by ATLAS. It is critical for
Volvo to control ATLAS output both in termof
safety and reliability. Furthermore, Artificial
Intelligence will determine the quality of
tomorrow’s car and become a product
differentiator. The manufacturer will design car
that make the driver feel good about delegating
more and more responsibilities to the
technology platform available.
Rear radar Rear radar is not used yet; however the current
technology can be applied to that specific purpose.
The currently radar technology with adaptive cruise
control is € 2250 (Volvocar.com). Therefore,
including development cost for fitting the technology
at the rear of the car will increase the current feature
price yet increase its value.
Commodity - On-board radar technology is
worth 19.4 billion US dollars, Infeneon has
9.4% of the world market (Infeneon.com).
Hence, it is not worth to customthis product.
Stirring wheel
motor vibrator
Mobil phone vibrators can be introduced within the
features of steering wheels. Four vibrators could be
used at 90 degree intervals to cope for various hand
locations (zero degree been horizontal to dash
board).
Commodity - Motor vibrators for phone are
used by all phone manufacturers. The online
cost is $3 per unit (Mrcellphoneparts.com).
Custom –Part of the steering wheel has to be
redesign to cater for the vibrators and ensure
that vibrations are perceptible through the
insulation materials.
9 Conclusion
ATLAS design phase has shown that drivers have
unknown needs with regards to urban driving. Their
on-road behaviour can be observed everyday at
traffic light were the majority of driver race from
light to light spending a considerable amount of
energy and compromising safety. The challenge with
developing ATLAS lies in the intelligent design of its
on-road behaviour with regards to the users’ level of
trust in driving assistance technology. Indeed the
current pool of users has never been exposed to such
technology. Therefore, the future of ATLAS relies on
delivering a technology with high performance
output. ATLAS has a remarkable future to improve
urban driving experience and Co2 reduction;
however it shall not be commercialised prior
extensive on-road testing. The development of
electric car will facilitates the application of such
technology has an electrical drive train can be easily
control (impulse propulsion instead of continuous),
furthermore it will increase the battery range
(eliminate waste of energy). The greatest challenge
facing ATLAS or similar technology using on-road
signalisation to regulate the car velocity is the
reliability of the digital signal. This means that
various actors will be required to engineer such
system. For instance, car manufacturer will need
digital providers such as Garmin, Google and so forth
to generate the digital map of the ‘real’ road signal. It
will also requires that road authority amend such
technology on the ground of traffic improvement,
accident reduction and pollution control.

ATLAS
Page 25 of 25
10 Index
ABS – Anti-locking Braking System
AI – Artificial Intelligence
AR – Augmented Reality
CAS – Collision Avoidance System
Co2 – Carbon Dioxide
ECU – Electronic Control Unit
GPS – Global Positioning System
KERS – Kinetic Energy Recovery System
PCM – Powertrain Control Module
RTL – Red Traffic Light
SS – Screen Shot
RT – Reaction Time
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