Self
Leveraging Smartphone Cameras for Collaborative Road Advisories
Submitted by R.S.Shenilton & M.Banu Chandhar
Abstract :
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 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 0.66s, for pre-timed traffic signals and within 2.45s, 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%, on average.
Index Terms—smartphone, camera, Intelligent Transportation Systems, services, traffic signal, detection, filtering, prediction, collaboration
INTRODUCTION
With an ever richer set of sensors, increased computational power and higher popularity, smartphones have become a major collaborative sensing platform. In particular, smartphones have been widely used to sense their environment and provide services to assist drivers. Several systems have been proposed that leverage smartphone GPS, accelerometer and
Submitted by R.S.Shenilton & M.Banu Chandhar
Abstract :
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 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 0.66s, for pre-timed traffic signals and within 2.45s, 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%, on average.
Index Terms—smartphone, camera, Intelligent Transportation Systems, services, traffic signal, detection, filtering, prediction, collaboration
INTRODUCTION
With an ever richer set of sensors, increased computational power and higher popularity, smartphones have become a major collaborative sensing platform. In particular, smartphones have been widely used to sense their environment and provide services to assist drivers. Several systems have been proposed that leverage smartphone GPS, accelerometer and
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