TrafficView: A Driver Assistant Device for Traffic Monitoring based on Car-to-Car Communication Sasan Dashtinezhad, Tamer Nadeem Department of CS, University of Maryland Bogdan Dorohonceanu, Cristian Borcea, Porlin Kang, Liviu Iftode Department of CS, Rutgers University

2 How to Provide Dynamic, Real-Time View of the Traffic Ahead a Car? What’s in front of that bus ? What’s behind the bend ? On rainy days On foggy days

3 TrafficView Use vehicle-to-vehicle ad hoc networks Use vehicle-to-vehicle ad hoc networks Vehicles have embedded computer, GPS receiver, and short-range wireless interface (IEEE ) Vehicles have embedded computer, GPS receiver, and short-range wireless interface (IEEE )

4Outline Introduction Introduction TrafficView Architecture TrafficView Architecture Road Identification Road Identification Data Aggregation Data Aggregation Prototype Implementation Prototype Implementation Experimental Results Experimental Results Conclusions and Future Work Conclusions and Future Work

5 TrafficView Architecture Receive data from remote vehicle Non-validateddataset Validateddataset Local data Display Broadcastdata NavigationModuleValidationModule AggregationModule

6 Determining the Position of Vehicles Problem: How to determine the road on which a vehicle moves? Problem: How to determine the road on which a vehicle moves? Solution: Use GPS data, a digital road map, “smoothing” techniques to reduce GPS errors, and Peano keys for fast lookup Solution: Use GPS data, a digital road map, “smoothing” techniques to reduce GPS errors, and Peano keys for fast lookup

7 Road Representation TIGER ® road maps from U.S. Census Bureau (publicly available) TIGER ® road maps from U.S. Census Bureau (publicly available) –RT1 files: road end points –RT2 files: road inner points –We subdivide the road segments into equally distant reference points for location precision

8 Road Identification Identify closest points to GPS location (using Peano keys) Identify closest points to GPS location (using Peano keys) Maintain short history of identified roads Maintain short history of identified roads Match GPS movement segment with closest road (angle less than 15 degrees) Match GPS movement segment with closest road (angle less than 15 degrees) 100% road identification for 4 sample routes in New Jersey 100% road identification for 4 sample routes in New Jersey

9Outline Introduction Introduction TrafficView Architecture TrafficView Architecture Vehicle Position Vehicle Position Data Aggregation Data Aggregation Prototype Implementation Prototype Implementation Experimental Results Experimental Results Conclusions and Future Work Conclusions and Future Work

10 Data Aggregation for Scalable Information Dissemination Problem: How to disseminate information about cars in dynamic ad-hoc networks of vehicles? Problem: How to disseminate information about cars in dynamic ad-hoc networks of vehicles? Solution: Broadcast all data in one packet (simple data propagation model) Solution: Broadcast all data in one packet (simple data propagation model) –Use aggregation to put as much data as possible in one packet –Aggregate data for vehicles that are close to each other –Perform more aggregation as distance increases –Maintain “ acceptable ” accuracy loss

11 Ratio-based Aggregation Current Vehicle Parameters Parameters – Aggregation ratio: inverse of the number of records that would be aggregated in one record – Portion value: amount of the remaining space in the broadcast message 3. For every region, merge every two consecutive records closer than the merge threshold 1. Calculate region boundaries 2. Calculate merge thresholds

12 Simulations NS-2 simulations NS-2 simulations –802.11b with 11Mbps bandwidth –transmission range of 250m –MTU = 2312 bytes –15,000m road, 4 lanes –300s duration of simulation Metrics Metrics –Visibility: average distance ahead about which a vehicle has information –Accuracy: average position error introduced by aggregation

13 Simulation Results High-density highway scenario: 870 vehicles, 30m/s average speed, 100m average gap High-density highway scenario: 870 vehicles, 30m/s average speed, 100m average gap VisibilityAccuracy

14 Prototype Implementation 3 cars on US Highway 1 in New Jersey Traffic Information Displayed in the Last Car

15 Experimental Results Visibility Accuracy 8 real GPS traces on a highway: 15m/s average speed, 200m average gap 8 real GPS traces on a highway: 15m/s average speed, 200m average gap

16Conclusions TrafficView provides drivers with real-time view of vehicles in front of them far beyond what they can physically see TrafficView provides drivers with real-time view of vehicles in front of them far beyond what they can physically see TrafficView is scalable and easy to deploy TrafficView is scalable and easy to deploy Developed accurate road identification software Developed accurate road identification software Designed and evaluated scalable aggregation algorithms Designed and evaluated scalable aggregation algorithms Implemented a prototype that works in real-life traffic conditions Implemented a prototype that works in real-life traffic conditions

17 Future Work Test prototype for larger scale networks Test prototype for larger scale networks Add query facilities Add query facilities Linear programming model to automatically calculate the aggregation parameters Linear programming model to automatically calculate the aggregation parameters Privacy and Trust Privacy and Trust

18 Thank you!