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

本项目利用移动传感和车辆定位技术，识别细粒度的异常驾驶行为，如交错、转向、快速u型转弯等，并进一步推断位置感知的危险车辆状态。现有的一些工作已经尝试通过预先部署的基础设施（如酒精传感器、红外传感器和摄像头）来检测驾驶员的状态，从而检测异常驾驶行为。这种方法需要额外的安装费用，因此难以广泛采用。为了构建无处不在的位置感知驾驶安全系统，本项目尝试采用低功耗传感（利用用户在车内携带的移动设备）和基于统计分析的学习技术，对车辆进行定位，识别细粒度的异常驾驶行为。更重要的是，所提出的系统可以持续跟踪驾驶员的行为，并确定与位置相关的细粒度危险车辆状态，例如在双向道路中心线行驶或长时间占用左侧车道。本项目旨在进行一项全面的研究，以了解目前的移动设备在多大程度上可以模拟各种现实世界的驾驶行为和相应的车辆动力学。为了解决驾驶安全问题，开发了一种新的实时移动传感系统，该系统将实时移动传感与异构驾驶环境相结合。最终的结果将是网络物理架构的持久原则，解决复杂环境的动态影响，并为物联网（iot）提供明确的指导方针。具体而言，研究人员从移动传感器读数中研究了能够描述每种异常驾驶行为的有效特征。通过提取这些特征，可以在考虑通用驾驶场景和异构移动设备的情况下，对车辆进行定位，并得出异常驾驶行为（如：交错、急转弯、快速掉头和突然刹车）的模式。开发了基于机器学习的技术来生成分类器模型，该模型可以清晰地识别细粒度的异常驾驶行为。进一步利用分类器模型作为基础，设计位置感知驾驶安全系统，利用低计算能力的移动设备实时跟踪用户的驾驶行为，实现与位置相关的危险车辆状态。

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.