Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Predicting the effects of in-car interfaces on driver behavior using a cognitive architecture
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Quiet calls: talking silently on mobile phones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
WALRUS: wireless acoustic location with room-level resolution using ultrasound
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Managing availability: Supporting lightweight negotiations to handle interruptions
ACM Transactions on Computer-Human Interaction (TOCHI)
The design and implementation of a self-calibrating distributed acoustic sensing platform
Proceedings of the 4th international conference on Embedded networked sensor systems
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
A robust statistical-based speaker's location detection algorithm in a vehicular environment
EURASIP Journal on Applied Signal Processing
BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices
Proceedings of the 5th international conference on Embedded networked sensor systems
Blindsight: eyes-free access to mobile phones
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tracking vehicular speed variations by warping mobile phone signal strengths
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Undistracted driving: a mobile phone that doesn't distract
Proceedings of the 12th Workshop on Mobile Computing Systems and Applications
Real-time system for monitoring driver vigilance
IEEE Transactions on Intelligent Transportation Systems
How long to wait?: predicting bus arrival time with mobile phone based participatory sensing
Proceedings of the 10th international conference on Mobile systems, applications, and services
MARVEL: multiple antenna based relative vehicle localizer
Proceedings of the 18th annual international conference on Mobile computing and networking
Push the limit of WiFi based localization for smartphones
Proceedings of the 18th annual international conference on Mobile computing and networking
Helping mobile apps bootstrap with fewer users
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A case for automatic sharing over social networks
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
Indoor pseudo-ranging of mobile devices using ultrasonic chirps
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
The case for psychological computing
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
Sensing vehicle dynamics for determining driver phone use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Proceedings of the 19th annual international conference on Mobile computing & networking
Acoustical ranging techniques in embedded wireless sensor networked devices
ACM Transactions on Sensor Networks (TOSN)
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
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This work addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements. Our detection system leverages the existing car stereo infrastructure, in particular the speakers and Bluetooth network. Our acoustic approach has the phone send a series of customized high frequency beeps via the car stereo. The beeps are spaced in time across the left, right, and if available, front and rear speakers. After sampling the beeps, we use a sequential change-point detection scheme to time their arrival, and then use a differential approach to estimate the phone's distance from the car's center. From these differences a passenger or driver classification can be made. To validate our approach, we experimented with two kinds of phones and in two different cars. We found that our customized beeps were imperceptible to most users, yet still playable and recordable in both cars. Our customized beeps were also robust to background sounds such as music and wind, and we found the signal processing did not require excessive computational resources. In spite of the cars' heavy multi-path environment, our approach had a classification accuracy of over 90%, and around 95% with some calibrations. We also found we have a low false positive rate, on the order of a few percent.