ParkNet: Vehicle Information Systems and Methods
Ultrasonic sensor fitted on the side of a car detects parked cars and vacant spaces.
ParkNet is a mobile system comprising vehicles that collect parking space occupancy information while driving by. Each ParkNet vehicle is equipped with a GPS receiver and a passenger-side-facing ultrasonic rangefinder to determine parking spot occupancy. The data is aggregated at a central server, which builds a real-time map of parking availability and could provide this information to clients that query the system in search of parking.
Creating a spot-accurate map of parking availability challenges GPS location accuracy limits. To address this need, inventors at Rutgers University have devised an environmental fingerprinting approach to achieve improved location accuracy. Based on 500 miles of road-side parking data collected over two months, they found that parking spot counts are 95% accurate and occupancy maps can achieve over 90% accuracy. Finally, they quantified the amount of sensors needed to provide adequate coverage in a city. Using extensive GPS traces from over 500 San Francisco taxicabs, it was demonstrated that if ParkNet were deployed in city taxicabs, the resulting mobile sensors would provide adequate coverage and be more cost-effective by a factor of roughly 10-15 when compared to a sensor network with a dedicated sensor at every parking space, as is currently being tested in San Francisco.
Provides real time information about the level of parking space availability on nearby streets to parking garages to allow them to dynamically tune their prices for parking in time.
Low cost time-saving method, less time spent looking for parking saves gallons of gasoline and greatly reduces prodution of carbon dioxide.
Intellectual Property & Development Status:
US 9,123,245; this technology is available for licensing and/or sponsorship of future developments.
- Suhas Mathur, Sanjit Kaul, Marco Gruteser, Wade Trappe. ParkNet: Harvesting Real-Time Vehicular Parking Information Using a Mobile Sensor Network. The S3 Workshop at the 10th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2009.
- Suhas Mathur, Tong Jin, Nikhil Kasturirangan, Janani Chandrashekharan, Wenzhi Xue, Marco Gruteser and Wade Trappe, "ParkNet: Drive-by Sensing of Road-side Parking Statistics", Proceedings of the 8th ACM/USENIX Annual International Conference on Mobile Systems, Applications and Services (MobiSys), to appear 2010.