TrafficView: A Driver Assistant Device for Traffic Monitoring

based on Car-to-Car Communication

Sasan Dashtinezhad, Tamer NadeemSasan Dashtinezhad, Tamer NadeemDepartment of CS, University of MarylandDepartment of CS, University of Maryland

Bogdan Dorohonceanu, Bogdan Dorohonceanu, Cristian Cristian BorceaBorcea, ,

Porlin Kang, Liviu IftodePorlin Kang, Liviu IftodeDepartment of CS, Rutgers UniversityDepartment of CS, Rutgers University

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How to Provide Dynamic, Real-How to Provide Dynamic, Real-Time View of the Traffic Ahead a Time View of the Traffic Ahead a

Car?Car?

What’s in What’s in front of front of

that bus ?that bus ?

What’s What’s behind behind

the the bend ?bend ?On rainy days

On foggy daysOn foggy days

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TrafficViewTrafficView

Use vehicle-to-vehicle ad hoc networksUse vehicle-to-vehicle ad hoc networks Vehicles have embedded computer, GPS receiver, Vehicles have embedded computer, GPS receiver,

and short-range wireless interface (IEEE 802.11)and short-range wireless interface (IEEE 802.11)

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OutlineOutline

IntroductionIntroduction TrafficView ArchitectureTrafficView Architecture Road IdentificationRoad Identification Data AggregationData Aggregation Prototype ImplementationPrototype Implementation Experimental ResultsExperimental Results Conclusions and Future WorkConclusions and Future Work

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TrafficView ArchitectureTrafficView Architecture

Receive data fromReceive data fromremote vehicleremote vehicle

Non-validatedNon-validateddatasetdataset

ValidatedValidateddatasetdataset

Local dataLocal data

DisplayDisplay

Broadcast Broadcast datadata

Navigation Navigation ModuleModule

Validation Validation ModuleModule

AggregationAggregationModuleModule

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Determining the Position of Determining the Position of VehiclesVehicles

Problem: How to determine the road on Problem: How to determine the road on

which a vehicle moves? which a vehicle moves?

Solution: Use GPS data, a digital road Solution: Use GPS data, a digital road

map, “smoothing” techniques to reduce map, “smoothing” techniques to reduce

GPS errors, and Peano keys for fast GPS errors, and Peano keys for fast

lookuplookup

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Road RepresentationRoad Representation

TIGERTIGER®® road maps from U.S. road maps from U.S. Census Bureau (publicly Census Bureau (publicly available)available)

– RT1 files: road end pointsRT1 files: road end points

– RT2 files: road inner pointsRT2 files: road inner points

– We subdivide the road segments We subdivide the road segments

into equally distant reference into equally distant reference

points for location precisionpoints for location precision

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Road IdentificationRoad Identification

Identify closest points to GPS location (using Identify closest points to GPS location (using Peano keys)Peano keys)

Maintain short history of identified roadsMaintain short history of identified roads Match GPS movement segment with closest Match GPS movement segment with closest

road (angle less than 15 degrees)road (angle less than 15 degrees) 100% road identification for 4 sample routes 100% road identification for 4 sample routes

in New Jerseyin New Jersey

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OutlineOutline

IntroductionIntroduction TrafficView ArchitectureTrafficView Architecture Vehicle PositionVehicle Position Data AggregationData Aggregation Prototype ImplementationPrototype Implementation Experimental ResultsExperimental Results Conclusions and Future WorkConclusions and Future Work

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Data Aggregation for Scalable Data Aggregation for Scalable Information DisseminationInformation Dissemination

Problem: Problem: How to disseminate information about How to disseminate information about

cars in dynamic ad-hoc networks of vehicles?cars in dynamic ad-hoc networks of vehicles?

Solution: Solution: Broadcast all data in one packet Broadcast all data in one packet

(simple data propagation model)(simple data propagation model)

– Use aggregation to put as much data as possible in Use aggregation to put as much data as possible in

one packetone packet

– Aggregate data for vehicles that are close to each Aggregate data for vehicles that are close to each

otherother

– Perform more aggregation as distance increasesPerform more aggregation as distance increases

– Maintain “acceptable” accuracy lossMaintain “acceptable” accuracy loss

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Ratio-based AggregationRatio-based Aggregation

Current VehicleCurrent Vehicle

ParametersParameters– Aggregation ratio: inverse of the Aggregation ratio: inverse of the number of records that would be number of records that would be aggregated in one recordaggregated in one record– Portion value: amount of the remainingPortion value: amount of the remaining space in the broadcast messagespace in the broadcast message

3. For every region, merge every two consecutive records closer than the merge threshold

1. Calculate region boundaries1. Calculate region boundaries

2. Calculate merge2. Calculate merge thresholdsthresholds

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SimulationsSimulations

NS-2 simulationsNS-2 simulations– 802.11b with 11Mbps bandwidth802.11b with 11Mbps bandwidth

– transmission range of 250mtransmission range of 250m

– MTU = 2312 bytesMTU = 2312 bytes

– 15,000m road, 4 lanes15,000m road, 4 lanes

– 300s duration of simulation300s duration of simulation

MetricsMetrics– Visibility: average distance ahead about which a Visibility: average distance ahead about which a

vehicle has informationvehicle has information

– Accuracy: average position error introduced by Accuracy: average position error introduced by aggregationaggregation

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Simulation ResultsSimulation Results

High-density highway scenario: 870 vehicles, High-density highway scenario: 870 vehicles, 30m/s average speed, 100m average gap30m/s average speed, 100m average gap

Visibility Accuracy

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Prototype ImplementationPrototype Implementation

3 cars on US Highway 1 in New Jersey

Traffic InformationDisplayed in the

Last Car

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Experimental ResultsExperimental Results

Visibility Accuracy

8 real GPS traces on a highway: 15m/s 8 real GPS traces on a highway: 15m/s average speed, 200m average gapaverage speed, 200m average gap

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ConclusionsConclusions

TrafficView provides drivers with real-time TrafficView provides drivers with real-time

view of vehicles in front of them far beyond view of vehicles in front of them far beyond

what they can physically seewhat they can physically see

TrafficView is scalable and easy to deployTrafficView is scalable and easy to deploy

Developed accurate road identification Developed accurate road identification

softwaresoftware

Designed and evaluated scalable aggregation Designed and evaluated scalable aggregation

algorithmsalgorithms

Implemented a prototype that works in real-Implemented a prototype that works in real-

life traffic conditionslife traffic conditions

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Future WorkFuture Work

Test prototype for larger scale networksTest prototype for larger scale networks

Add query facilitiesAdd query facilities

Linear programming model to automatically Linear programming model to automatically

calculate the aggregation parameterscalculate the aggregation parameters

Privacy and TrustPrivacy and Trust

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Thank you!Thank you!

http://discolab.rutgers.edu/traffic/