Signal guru

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SignalGuru: Leveraging Mobile Phones for Collaborative Traffic Signal Schedule Advisory

Emmanouil Koukoumidis , Li- Shiuan Peh, Margaret MartonosiPrinceton University

MIT

BOURSINOS CHRISTOS/DISTRIBUTED SYSTEMS/ Msc CS FALL 2011/V. KALOGERAKH

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Traffic signals despite that they are a safety control 1. Enforce a stop and go movement2. Increases fuel consumption3. Reduces traffic flow 4. Traffic jams

For all problems that we saw before we have some solutions5. Countdown timer at vehicular traffic signals ( Komotini,

Alexandroupoli)6. Countdown timers for pedestrian traffic signals(USA) 7. GLOSA


Traffic conditions

results from CAMBRIDGE and SINGAPORE.


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We can alleviate this problem with computational devices3. GLOSA (Green Light Optimal Speed Advisory)

• detect current traffic signals with their camera • collaboratively communicate• Learn traffic signal results patterns• Predict their future schedule

AUDI recently prototyped a small scale DSRC-based GLOSA system for 25 traffic signals in Ingolstadt

It is expensive to equip all the cars with such technology.

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Where we put our system in our car.


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GLOSA faces several challenges:1. Lack of Loop detector information ( traffic signals based on

information from loop detectors embedded under every lane on roads close to the stop line governed by traffic signals)

2. Commodity cameras ( the quality of smart phones cameras)3. Limited processing power ( takes significant computational

resources)4. Uncontrolled environment composition and false detection

(no control over the composition of the content captured by their video cameras)

5. Variable ambient light conditions 6. Need for collaboration ( not be able to see a far-away traffic

signal, or may not be within view of the traffic signal for a long enough stretch of time)


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GLOSAThe goal of GLOSA is to advise drivers on the optimal Speed they

should maintain so that the signal is green when they arrive at the next intersection.

What offers1. Decreased fuel consumption2. Smoothed and increased traffic flow3. Decrease environment impactNeed four pieces 4. The residual amount of time till the traffic signal changes5. The intersection location(map)6. Vehicles correct location( gps ) 7. The queue length of the traffic ahead.May improve an individual vehicles travel time.



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Other Possible SignlaGuru-Enabled applications1. Traffic Signal-Adaptive Navigation(TSAN)2. Red Light Duration Advisory(RLDA)3. Imminent Red Light Advisory(ILVA)4. Red Light Violation Advisory (RLVA)

In the next slide we see the deference between TSAN and GLOSA.


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Compare GLOSA-TSAN• The deference

is that in GLOSA we have the optimal speed and in TSAN we have a suggestion detour.

• Architecture of GLOSA

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SIGNALGURU ARCHITECTUREDetection Module/ detection algorithm

• Which color qualify

• Best candidate to be a traffic signal

• BCC percentage of the pixels fall into the correct color range

• How many pixel are dark enough to qualify as traffic signal


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How the device do the detection. COLOR FILTER


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IMU-based Detection Window



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With what angles do the detection

• Angle θ=φ/(2-χ) angle χ =ψ-ω angle ψ=arctan(hs-hc)/d

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Variable Ambient Light ConditionsOur program will not perform good in some cases1. Time of day2. Weather conditions

To solve this problem we change the sensitivity of the mobile camera to more sensitive.

We have some buttons to do this job in our program


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Transition filtering module

filters

We filter RG transition using a two-stage filter:1. Low Pass Filter(LPF) in 1st stage2. Collocation filter in 2nd stage


LPF 1st stageIn 88% of the cases, false positive detections occur over a single

frame and do not spread over multiple consecutive frame:1. R…RGR…RWhen a car is waiting at the red light it correctly detects, then at a

specific instance it misdetects a passing object for a green traffic light.

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LPF 1st stage

2. G…GRG…GWhen the vehicle misdetects an arbitrary object for a red light

between detections of the actual green light.3. NS…NSRGNS...NSWhen the view of the car is obstructed and there is no traffic

signal in sight. However in some point it misdetects an arbitrary object for a R and G light.

The LPF classifies only this transmission RRGG

Colocation filter 2nd stageChecks whether the green bulb that was just detected is close to

the red bulb detected in the previous frame.


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Collaboration module

A node(cell phone) needs information about a traffic signal well before the signal comes into the node’s camera field.

The collaboration module allows participating SignalGuru nodes to opportunistically exchange their traffic signal information by periodically broadcasting UDP packets in 802.11 ad-hoc.

So they predict the schedule by using a database of the traffic signal settings for a period of time.


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PREDICTION MODULE

We have two main categories of traffic signal:1. Pre-timed traffic signals2. Traffic-adaptive traffic signals

Because their operation is very different , SignalGuru uses different prediction schemes for each category


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Pre-timed traffic signals

SignalGuru’s prediction module maintains a database of the traffic signal settings.

This means that SignalGuru knows how long each phase lasts.The Challenge is to synchronize the SygnalGuru clock with the

time of phase transition of a traffic signal.


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Pre-timed traffic signals

Light switch to red (phase A) switch to Green (phase B)Phase A will follow after phase B.If we have a false detection the synchronize needs to be

reestablished.


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Traffic-adaptive traffic signals

SignalGuru predicts future transitions by detecting past transitions and predicting the length of the current of next phases.

The key different from the prediction of pre-timed traffic signals lies in the prediction of the phase length, as opposed to looking it up from a database.

SignalGuru predicts the length of a phase by measuring and collaboratively collecting the prior traffic signal transition history and feeding it to a Support Vector Regression(SVR) prediction model.

We evaluate the prediction performance of different Prediction Schemes(PS) by training the SVR with different sets of features.


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Traffic-adaptive traffic signals

One-week-history long of data is enough to train the SVR model.Furthermore the SVR model does not need to get continuously

re-trained. Re-training the model every 4 to 8 months is frequent enough in order to keep the prediction errors small.


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CAMBRIDGE DEPLOYMENT• 5 cars with i-phone and

the drivers follow the rout.

• One more device P2 pedestrian.

• P2 was the ad-hoc data relay node.


METHODOLOGY

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SINGAPORE DEPLOYMENT• 2 cars following two

routs.• 8 i-phones in taxis• 5 mobile in rout A• 3 mobile in rout B• One more device P2

pedestrian.• P2 was the ad-hoc data

relay node. Also recording the ground truth when the traffic signals status transitioned.


METHODOLOGY

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SIGNALGURU EVALUATION

TRAFFIC SIGNAL DETECTION


We evaluate the performance of two deployments.

• 5959 frames Cambridge• 1352 frames Singapore

• False 7,8% Cambridge• False 12,4% Singapore

• Correctly 92,2% Cambridge

• Correctly 87,6% Singapore

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We evaluate the benefits that IMU-based detection window offers.

• The IMU reduces significantly the number of red false positives.

• Often confuses vehicles for a light.

• Reduces the average processing time by 41%

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Transition Filtering


The performance of the transition Filtering.

The False Positive(FP) in Cambridge is smaller than in Singapore

1. Rate of FP traffic signal detection is smaller in Cambridge

2. The average waiting time at red traffic signals is only 19,7s in C. vs 47,6s in S.

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Schedule Prediction


Cambridge deployment• Pre-timed with average

error 0,66s• Video frame every T=2sec• Εmax=T/2 • EXP=T/4=o,5 sec • Can effectively support

the accuracy requirements of all applications.

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Schedule Prediction


Singapore deployment• Average error 2,45 sec

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GLOSA Fuel Efficiency


• From OBD-LINK

• FROM P1P2• Reducing fuel

consumption on average by 20,3%

• Improves the vehicle’s mileage on average by 24,5%

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Related works


• In Lee “et al” propose an application that lets police track the movement of suspicious vehicles based on information sensed by camera-equipped vehicles.

• Other works proposed to equip vehicles with specialized cameras and detect traffic signals with the ultimate goal of enabling autonomous driving , assisting the driver, detecting the location of intersection and overlaying navigation information.

• Our System GLOSA putting the IMU and also it has safe results. It use DLT certificates or a TPM in order to ensure trust in the exchange of traffic signal data.

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Conclusions


• In order to predict traffic signals future schedule and support a set of novel applications is a fully distributed and grassroots approach.

• Our proposed schemes improve traffic detection filter noisy traffic signal data and predict traffic signal schedule.

• SignalGuru can effectively predict the schedule for not only pre-timed but also state of the art traffic-adaptive traffic signals.

• Fuel efficiency

We hope for a motivation from you.

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The End


Date post:	19-Jun-2015
Category:	Education
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