CRII: NeTS: Ubiquitous Sensing based Location-aware Driving Safety System

Suny At Binghamton, Binghamton NY

Investigators

Abstract

This project exploits mobile sensing and vehicle localization to identify fine-grained abnormal driving behaviors, such as weaving, swerving, and fast U-turns, and further to infer location-aware dangerous vehicular status. Several existing works have tried to detect abnormal driving behaviors by focusing on detecting drivers' status based on pre-deployed infrastructure, such as alcohol sensors, infrared sensors, and cameras. Such approaches incur extra installation cost and are thus difficult to be widely adopted. In order to build pervasive location-aware driving safety systems, this project tries to deploy low power consumption sensing (utilizing mobile devices carried by users in vehicles) and learning techniques based on statistical analysis to localize vehicles and identify fine-grained abnormal driving behaviors. More importantly, the proposed system keeps tracking the drivers' behaviors and determines fine-grained location-related dangerous vehicular status, such as driving on the center line of two-way roads or occupying left lanes for a long time. This project seeks to conduct a comprehensive study to understand to what extent the current mobile devices can model various real-world driving behaviors and corresponding vehicle dynamics. A new real-time mobile sensing system, which combines real-time mobile sensing and heterogeneous driving environments, is developed to address driving safety concerns. The final results will be the abiding principles of cyber-physical architecture that resolve dynamic impacts of complex environments and provide clear guidelines over Internet of Things (IoTs). Specifically, effective features are investigated from mobile sensor readings that are able to depict each type of abnormal driving behaviors. These features can thus be extracted to localize the vehicles and derive the patterns of abnormal driving behaviors (e.g., weaving, swerving, fast U-turn, and sudden breaks) with the consideration of generic driving scenarios and heterogeneous mobile devices. Techniques based on machine learning are developed to generate a classifier model that could clearly identify fine-grained abnormal driving behaviors. The classifier model will be further utilized as a foundation to devise the location-aware driving safety system, which can track users' driving behaviors and realize location-related dangerous vehicular status in real-time using low-computing-capability mobile devices.