MRI: CloudCar: Development of a Diverse Distributed Instrument for Vehicles in the Cloud

提案编号：12- 29700 PI：Dantu，Ram; Tam，大卫N机构：北德克萨斯大学标题：MRI/开发：CloudCar：一个多样化的分布式仪器在云中的车辆项目建议：这个项目，开发一个被称为CloudCar的仪器，旨在建立一个基于云的基础设施，以监测和收集有关司机，车辆和道路状况的数据。要收集的数据类型包括驾驶员的生物特征（眼球跟踪、心率、血压和EEG脑电波分析）、来自车辆的传感数据、交通数据和关于道路状况的传感数据。收集的数据在云端进行分析，并用于实现广泛的应用，包括评估驾驶员的行为和驾驶能力，车辆的“健康状况”，传播有关道路状况的信息以防止事故和道路拥堵，以及车辆碰撞检测和通知。所要求的基本设备包括云服务器、软件和调谐工具、车载诊断系统II和CAN软件/硬件、用于在驾驶过程中进行α、β和γ波分析的若干14通道脑电图设备、脑电图头带和移动的设备。Ava汽车将被开发，以允许研究人员和应用程序开发人员访问有关虚拟汽车的信息。这项工作扩展了当前的基础设施，包括用于实时监控和反馈的云。成功地将云集成到系统中对于提高系统的准确性和可靠性具有巨大的潜力。该提案提出了多个研究问题，并确定了研究方向，以便在拟议的基础设施可用时解决这些问题。将开展以下研究活动：-设计一个包含所有事件时间轴的AvaCar车辆和道路状况门户网站;-使用移动的电话、OBDII和CAN总线测量驾驶员注意力和车辆状况;-使用基于EEG的头带测量驾驶员分心情况;-设计一个基础设施，用于向某个区域的驾驶员提供近实时通知和警报;- 为驾驶员提供实时指导，以实现最佳油耗、节能定位和速度跟踪; -主动预测驾驶员？事故、危险和施工期间的行为;以及，-V2 C2 V的性能分析、灵敏度分析和校准。更广泛的影响：所提出的仪器可以极大地影响评估和/或改善残疾人、老年人、青少年驾驶员、物理治疗师和适当的体育锻炼的驾驶性能的能力。总的来说，该项目有助于人身和道路安全，优化燃料消耗和节能本地化。该项目将基础设施的使用纳入所涉不同机构的课程。课程内容，重点是云计算和车载通信，将开发和定制的计算机科学，电气工程，机械工程和认知科学计划在参与部门。研究团队将建立在他们现有的推广和教育计划，以吸引不同的本科生和研究生，并让他们参与MRI项目。

Proposal #: 12-29700PI(s): Dantu, Ram; Tam, David NInstitution: University of North Texas Title: MRI/Dev.: CloudCar: A Diverse Distributed Instrument for Vehicles in the CloudProject Proposed:This project, developing an instrument referred to as CloudCar, aims to build a cloud-based infrastructure to monitor and collect data about drivers, vehicles, and road conditions. The type of data to be collected includes driver's biometrics (eyeball tracking, heart rate, blood pressure, and EEG brain wave analysis), sensory data from the vehicle, traffic data, and sensed data about road condition. The collected data is analyzed in the cloud and used to enable a wide spectrum of applications, including assessing drivers' behavior and ability to drive, the "health" of the vehicle, dissemination of information about road conditions to prevent accidents and road congestion, and vehicular crash detection and notification. The basic equipment requested includes cloud servers, software and tuning tools, the OBD-II and CAN software/hardware, a number of 14-channel EEG equipment for the alpha, beta, and gamma wave analysis during driving, EEG headbands, and mobile devices. AvaCars will be developed to allow researchers and application developers to access information about virtual cars. This work extends current infrastructure to include the cloud for real-time monitoring and feedback. A successful integration of the cloud into the system has great potential to enhance the accuracy and reliability of the system. The proposal raises multiple research questions and establishes research directions that can be pursued to address these questions when the proposed infrastructure is made available. The following research activities will be pursued: - Design of a portal with AvaCar of a vehicle and road conditions with all the timeline of events;- Measurements of driver attention and vehicle condition using mobile phones, OBDII, and CAN bus;- Measurements of the driver distraction using EEG-based headband;- Design of an infrastructure for near real-time notifications and alerts to drivers in an area;- Real-time guidance to the driver for optimal fuel consumption, energy-efficient localization, and speed tracking; - Proactive prediction of drivers? behavior during accidents, hazards, and construction; and,- Performance analysis, sensitivity analysis, and calibration for V2C2V.Broader Impacts: The proposed instrument can greatly impact the capability to assess and/or improve the driving performance of people with disabilities, senior citizens, teen drivers, physiotherapists, and proper physical exercises. Overall, the project contributes to personal and road safety, optimized fuel consumption, and energy-efficient localization. The project incorporates the use of the infrastructure in the curricula of the different institutions involved. Course content, focused on cloud computing and vehicular communications, will be developed and tailored to computer science, electrical engineering, mechanical engineering, and cognitive science programs at participating departments. The research team will build on their existing outreach and educational programs to attract diverse undergraduate and graduate students and involve them in the MRI project.