atlas design phase

ATLAS

Prepared by – Cedric Melin s3347116

ATLAS

Advanced Traffic Light Approach System

-

Design Phase

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ATLAS


Table of Contents

Table of Contents......................................................................................................................... 2

Executive summary...................................................................................................................... 2

1 Background......................................................................................................................... 3

2 User profile and requirements...................................................................................................4

3 Current market, Opportunities to excel and Customers....................................................................5

4 Objectives tree..................................................................................................................... 6

5 Statement of Requirement.......................................................................................................7

6 House of Quality................................................................................................................... 9

7 Concept Generation and Selection...........................................................................................11

8 Detail design...................................................................................................................... 17

9 Conclusion........................................................................................................................ 21

10 Index............................................................................................................................. 22

11 Reference....................................................................................................................... 22

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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.

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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.

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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.

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Object 2

Figure 1 - ATLAS preliminary survey

+Law

+ CostMature MarketMarketNode

6.

1.

2. 3.

4. 5.

AI

7.

ATLAS


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.

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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 actCustomer

Constraints

ATLAS


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.

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ATLAS


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- % of collision at 60 km/h- % of collision at 50 km/h- % of collision at 40 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

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- Redesign and test until satisfaction is cost-effective

RTA Road traffic acts

Use

r/D

river

Constraints

Up

Unit of measurement Preferred direction

Volv

o

Goal – Intelligent management of traffic light time windows to improve driving experience and Co2 reduction

UpUp

DownUp

DownDown

CT1CT2

gr of Co2/kmCT3CT4

Sense of controlLess work

Cost

User friendly

ObjectivesDriver attention

Passive safetyEnvironmentally Friendly

% of new components

CT5

Table 1 - Statement of Requirements

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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 correlationWhy – 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 correlationWhy – 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 correlationWhy – 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.

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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.

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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

Driv

er

(6)

% o

f new

co

mpo

nent

s

Volv

o

(7) C

T5

TR Importance

Tech

nica

l Re

quire

men

ts (T

R)

(3) (

gr o

f Co2

/km

)

(1) C

T1

(5) C

T4

(2) C

T2

(4) C

T3

ATLAS


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.

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System 1Traffic Light

System 2Driver/car

System 1Brain

System 2Heart/artery

Courtesy: Wikipedia

ATLAS


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 SeatSeat belt

Gyroscope KERSSteering wheel Gearbox Rotation

Gear stick Pedal AntennaForce Emotion

CameraTraffic light

SpeedoTorque Stress

Pleasure Audio HapticVisual Smell

Time Close loopOpen loop Object

Road

Distance KE PEWork

Slope

Day Glare NightRain $

Co2

HUD Cartesian DeathGPS

Wiper

Memory Acceleration DecelerationRadar

Momentum

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c) System morphological analysis to generate concepts–

System 1 - System 2 –

System 3 –$

System 4 –$

System 5 – System 6 –

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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. vii) Weighted Decision Matrix

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ATLAS


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.

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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 93 171 109 510.55 0.39 0.38 0.70 0.45 0.21

Table 2 - Weighted Decision Matrix (WDM)

Concept

CR Im

port

ance

Sum to full potentialSum

Use

rVo

lvo

ATLAS


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 functionAvoided 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 ...

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http://www.faqs.org/patents/app/20130063281

11

Figure 1 -

Set 1

Set 2 Set 3

ATLAS


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 –

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1

Figure 2 -

2

4

65

GPS TLM

Set point desired energy Input

Traffic sensors

+

+

Energy Output

Driver sensors

- -

93

Drive train-

7

8

Traffic sensors

+ 10

ATLAS


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.

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ATLAS


d) Embodiments (Flow charts)

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Figure 3 - Flow charts terminology

ATLAS


d) Embodiments (Flow charts) continued …

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ATLAS


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.

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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 term of 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 custom this 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.

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10 Index

ABS – Anti-locking Braking SystemAI – Artificial IntelligenceAR – Augmented RealityCAS – Collision Avoidance SystemCo2 – Carbon DioxideECU – Electronic Control UnitGPS – Global Positioning SystemKERS – Kinetic Energy Recovery SystemPCM – Powertrain Control ModuleRTL – Red Traffic LightSS – Screen ShotRT – Reaction Time

11 Reference

Kent, B. (1995). Red light running behavior at red light camera and control intersections, Monarch University Accident Research Centre – Report#73-1995; http://www.monash.edu.au/miri/research/reports/muarc073.html viewed the 24/04/2013Jess3,Marshalite alternative traffic light, Information aesthetics, http://infosthetics.com/archives/2008/05/marshalite_alternative_traffic_light.html, viewed the 26/04/2013Wikipedia, Traffic Light, Wikipedia website, http://en.wikipedia.org/wiki/Traffic_light, viewed the 26/04/2013Territo, C (2013). St. Louis Red-Light Safety Cameras Changing Driver Behaviour, American Traffic Solution, http://www.atsol.com/st-louis-red-light-safety-cameras-changing-driver-behavior/, viewed the 21/04/2013PatentsDocs,Vehicle traffic signal notification system,Stay tuned to the technology, http://www.faqs.org/patents/app/20130063281, viewed the 25/04/2013Fallah, A. 2008 BMW Speed Limit Display a car that can read speed limit signs, caradvice, http://www.caradvice.com.au/15277/bmw-speed-limit-display-a-car-that-can-read-speed-signs/, viewed the 23/04/2013Smith, G. 2011 Smashing idea: Volvo installs pedestrian detection system that brakes car automatically, Dailymail, http:/www.dailymail.co.uk/sciencetech/article-1360672/Volvo, viewed the 27/04/2014Wikipedia,Google driverless car, Wikipedia website, http://en.wikipedia.org/wiki/Google_driverless_car, viewed the 27/04/2013Graham-Rowe, D. 2009 Head-up Displays go holographic, Technology review, http:/www.technologyreview.com/news/415755/head-up-displays-go-holographic/, viewed the 27/04/2013Costa, G. 2012 Stress of driving general overview, PubMed Jul-Sep 34(3);348-51Susillowati, H. 2012 Cognitive characteristics of older Japanese drivers, PubMed Feb29;31-2Scott, J.J. 2008 A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving, PubMed Apr 50(2) 264-75.Kramer, AF. 2007 Influence of age and proximity warning devices on collision avoidance in simulated driving, PubMed Oct 49(5) 935-49.Johnson, MJ. 2011Physiological responses to simulated and on-road driving, PubMed Sep 81(3) 203-8Hardesty, L. 2013 Researcher amplify variations in video, making the invisible visible, MIT news office, http://web.mit.edu/newsoffice/2012/amplifying-invisible-video-0622.html, viewed the 03/05/2013Infeneon website, 2008, Bosh to use Radar chip from Infeneon, Business and Financial Press, http://www.infineon.com/cms/en/corporate/press/news/releases/2008/INFATV200812-015.html, viewed the 16/05/2013Volvocar website, 2013, Volvo website, http://www.volvocars.com/ie/top/about/news/pages/default.aspx?itemid=15, viewed the 09/05/2013MrCellphonepart, 2013, http://www.mrcellphoneparts.com/mm5/merchant.mvc?Store_Code=MCP&Screen=CTGY&Category_Code=I4-Parts, viewed the 16/05/2013McCraken, H.2011, Tesla’s dream screen: The car dashboard of the future, CNet, http://news.cnet.com/8301-33200_3-57322693-290/teslas-dream-screen-the-car-dashboard-of-the-future/#!, viewed the 16/05/2013Beissmann, T. 2011, Volvo concept you unveiled at 2011 Frankfurt motor show, CarAdvice, http://www.caradvice.com.au/137766/volvo-concept-you-unveiled-at-2011-frankfurt-motor-show/, viewed the 16/05/2013PRLOG, 2008, Worldwide precision GPS market to grow from US 3 billion in 2008 to US6-8 billion by 2012, PRLOG, http://www.prlog.org/10120262-world-wide-precision-gps-market-to-grow-from-us3-billion-in-2008-to-us6-8-billion-by-2012.html,viewed the 16/05/2013YouTube, Mercedes-Benz ML350 safety features; http://www.youtube.com/watch?v=jYPULGbELF8, viewed the 19/05/2013

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http://news.cnet.com/8301-33200_3-57322693-290/teslas-dream-screen-the-car-dashboard-of-the-future/#!

http://news.cnet.com/8301-33200_3-57322693-290/teslas-dream-screen-the-car-dashboard-of-the-future/#!

http://www.mrcellphoneparts.com/mm5/merchant.mvc?Store_Code=MCP&Screen=CTGY&Category_Code=I4-Parts

http://www.mrcellphoneparts.com/mm5/merchant.mvc?Store_Code=MCP&Screen=CTGY&Category_Code=I4-Parts

http://www.volvocars.com/ie/top/about/news/pages/default.aspx?itemid=15

http://www.infineon.com/cms/en/corporate/press/news/releases/2008/INFATV200812-015.html

http://web.mit.edu/newsoffice/2012/amplifying-invisible-video-0622.html

http://en.wikipedia.org/wiki/Google_driverless_car

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http://en.wikipedia.org/wiki/Traffic_light

http://infosthetics.com/archives/2008/05/marshalite_alternative_traffic_light.html

http://www.monash.edu.au/miri/research/reports/muarc073.html

atlas design phase

Documents

traffic light encroachments

traffic light time windows

cedric melin s3347116

statement of requirement

common driving mode

aggressive driving mode

driver interviews

performance needs