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MRI: Acquisition of Automotive Tire Force and Moment Sensors

MRI：采集汽车轮胎力和力矩传感器

基本信息

批准号：
1726283
负责人：
Craig Beal
金额：
$ 15.48万
依托单位：
Bucknell University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726283&HistoricalAwards=false
关键词：
MRI Acquisition Automotive Tire Force

项目摘要

Significant investments are being made in intelligent roadway infrastructure; manufacturers are deploying increasingly advanced driver assistance systems; and researchers are racing to develop reliable autonomous vehicles that can take over the task of driving entirely. Despite these remarkable advances fundamental research is needed to develop accurate models of overall vehicle dynamics and potential feedback control for future autonomous and smart vehicle systems. To function effectively, autonomous vehicles must accurately sense and respond to changing roadway conditions. By supporting the acquisition of instrumented wheels-- to provide real-time data generated by the tires interacting with the road surface-- this Major Research Instrumentation award will contribute to U.S. competitiveness in the quickly evolving international automotive sector. These data will allow researchers at Bucknell University, Penn State University and other institutions to conduct experiments, share data and collaborate with others to develop algorithms to determine road conditions from sensors. The improvement in fundamental understanding of the interaction of individual tires operating in-situ with the road will enable continued advances in vehicle control, autonomous vehicle operation, and intelligent roadway systems. The instrumented wheels supported by this award are designed to capture all six forces and moments that are generated by the tire interacting with the road. A custom-manufactured adapter provides physical connection between the wheel rim and load cells, which in turn transfers tire forces and moments to the vehicle chassis. Wheel forces up to 24 kN and torques up to 7.2 kNm, which represent tire behaviors beyond even the most extreme skidding, can be measured with less than 0.1% error. The sensors will be installed on a drive-by-wire research vehicle with computer-controlled independent front steering and independent rear drive. The vehicle is instrumented with a dual-antenna GPS and inertial measurement sensors that provide measurements of the vehicle kinematics. By comparing wheel force and moment data to steering torque inputs, model predictions, vehicle motion measurements, and pavement measurements, the instrumented wheels enable advanced actuation strategies and unique methods of identifying road surface friction conditions. These data will enable designers to develop adaptive control systems that allow autonomous vehicles to better sense road conditions and to optimize the modeling and use of steering and braking inputs to improve vehicle safety, performance, and efficiency.

对智能道路基础设施进行了大量投资；制造商正在部署越来越先进的驾驶辅助系统；研究人员正在竞相开发可靠的自动驾驶汽车，以完全接管驾驶任务。尽管取得了这些显着的进步，但仍需要基础研究来开发整体车辆动力学的精确模型以及未来自动驾驶和智能车辆系统的潜在反馈控制。为了有效发挥作用，自动驾驶车辆必须准确感知并响应不断变化的道路状况。通过支持采集仪表化车轮（以提供轮胎与路面相互作用生成的实时数据），这项重大研究仪表奖将有助于提高美国在快速发展的国际汽车行业的竞争力。这些数据将使巴克内尔大学、宾夕法尼亚州立大学和其他机构的研究人员能够进行实验、共享数据并与其他人合作开发算法来通过传感器确定道路状况。对现场运行的单个轮胎与道路相互作用的基本理解的提高将使车辆控制、自动驾驶车辆操作和智能道路系统不断进步。该奖项支持的仪表化车轮旨在捕捉轮胎与路面相互作用产生的所有六种力和力矩。定制的适配器提供轮辋和称重传感器之间的物理连接，从而将轮胎力和力矩传递到车辆底盘。车轮力高达 24 kN，扭矩高达 7.2 kNm，这代表了轮胎的行为甚至超越了最极端的打滑，测量误差小于 0.1%。这些传感器将安装在一辆线控研究车辆上，该车辆具有计算机控制的独立前转向和独立后驱动装置。该车辆配备了双天线 GPS 和惯性测量传感器，可提供车辆运动学测量。通过将车轮力和力矩数据与转向扭矩输入、模型预测、车辆运动测量和路面测量进行比较，仪表化车轮可实现先进的驱动策略和识别路面摩擦条件的独特方法。这些数据将使设计人员能够开发自适应控制系统，使自动驾驶车辆能够更好地感知路况，并优化转向和制动输入的建模和使用，以提高车辆的安全性、性能和效率。