|Department:||Electrical and Computer Engineering|
|Keywords:||Distracted driving – Prevention; Cell phones and traffic accidents; Context-aware computing|
|Full text PDF:||http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068913|
Smart phones have extended the capability of a phone from "texting and calling" to a whole new dimension of context-awareness with the capabilities of on-device sensors. This is particularly useful in providing context-adaptive user-centric automated services, which is emerging as an area of much current research interest. In this work, we present the system design aspects pertaining to Context Awareness for Distraction-free Driving (CADD) system, a realization of context awareness and context-aware automated services, designed to mitigate in-vehicle distraction caused by cell phones. Context awareness in this work refers to the state of the user: Driving or Available. This state is detected using a Bluetooth and GPS sensor based scheme on the device. Along with the end to end system design, our focus has been on reducing latency, enabling privacy driven data sharing and attaining reliability, aimed at optimization of automated services. The design exploits the Google Cloud to Device Messaging framework and Amazon Web Services to enable efficient communication between clients and allow scaling to large numbers of users. For critical services as context updates, we have improved the reliability from a success rate less than 50% to greater than 90%, by introducing triple re-transmissions and network connectivity monitored re-tries for intermittently connected devices. Further, we have tested both GPS based and Network based updates for location services. By using an algorithm that combines both GPS and Network based updates, we have reduced the latency of obtaining the first location fix from a few seconds (> 4seconds) to less than a second (~500-800msec) with a deviation in path limited to 500-1000m. This scheme also improved performance with in-door locations where GPS-based updates fail. To address the location related privacy concerns, our design maintains no location history information and utilizes proximity metrics namely time and distance, as opposed to plainly exposing the geo-location of users on maps. Further, we have also designed the user interfaces iteratively based on feedback from test users. The user interface has been optimized to keep the depth of navigation to be less than 4 (measured as the number of clicks or the screens navigated to complete one service).