The Community for Technology Leaders
Green Image
Issue No. 05 - May (2012 vol. 11)
ISSN: 1536-1233
pp: 707-723
Emmanouil Koukoumidis , Princeton University, Princeton
Margaret Martonosi , Princeton University, Princeton
Li-Shiuan Peh , Massachussets Institute of Technology, Cambridge
Ubiquitous smartphones are increasingly becoming the dominant platform for collaborative sensing. Smartphones, with their ever richer set of sensors, are being used to enable collaborative driver-assistance services like traffic advisory and road condition monitoring. To enable such services, the smartphones' GPS, accelerometer, and gyro sensors have been widely used. On the contrary, smartphone cameras, despite being very powerful sensors, have largely been neglected. In this paper, we introduce a collaborative sensing platform that exploits the cameras of windshield-mounted smartphones. To demonstrate the potential of this platform, we propose several services that it can support, and prototype SignalGuru, a novel service that leverages windshield-mounted smartphones and their cameras to collaboratively detect and predict the schedule of traffic signals, enabling Green Light Optimal Speed Advisory (GLOSA) and other novel applications. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66 s, for pretimed traffic signals and within 2.45 s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3 percent, on average.
Smartphone, camera, intelligent transportation systems, services, traffic signal, detection, filtering, prediction, collaboration.
Emmanouil Koukoumidis, Margaret Martonosi, Li-Shiuan Peh, "Leveraging Smartphone Cameras for Collaborative Road Advisories", IEEE Transactions on Mobile Computing, vol. 11, no. , pp. 707-723, May 2012, doi:10.1109/TMC.2011.275
84 ms
(Ver 3.3 (11022016))