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车路协同硬件在环仿真框架:CarTest。

A Hardware-in-the-Loop V2X Simulation Framework: CarTest.

机构信息

College of Computer Science and Technology, Jilin University, Changchun 130012, China.

出版信息

Sensors (Basel). 2022 Jul 3;22(13):5019. doi: 10.3390/s22135019.

Abstract

Vehicle to Everything (V2X) technology is fast evolving, and it will soon transform our driving experience. Vehicles employ On-Board Units (OBUs) to interact with various V2X devices, and these data are used for calculation and detection. Safety, efficiency, and information services are among its core uses, which are currently in the testing stage. Developers gather logs during the real field test to see if the application is fair. Field testing, on the other hand, has low efficiency, coverage, controllability, and stability, as well as the inability to recreate extreme hazardous scenarios. The shortcomings of actual road testing can be compensated for by indoor testing. An HIL-based laboratory simulation test framework for V2X-related testing is built in this study, together with the relevant test cases and a test evaluation system. The framework can test common applications such as Forward Collision Warning (FCW), Intersection Collision Warning (ICW) and others, as well as more advanced features such as Cooperative Adaptive Cruise Control (CACC) testing and Global Navigation Satellite System (GNSS) injection testing. The results of the tests reveal that the framework (CarTest) has reliable output, strong repeatability, the capacity to simulate severe danger scenarios, and is highly scalable, according to this study. Meanwhile, for the benefit of researchers, this publication highlights several relevant HIL challenges and solutions.

摘要

车对一切(V2X)技术发展迅速,即将改变我们的驾驶体验。车辆使用车载单元(OBU)与各种 V2X 设备进行交互,这些数据用于计算和检测。安全、效率和信息服务是其核心用途之一,目前正处于测试阶段。开发人员在实际现场测试中收集日志,以观察应用程序是否公平。然而,现场测试效率低、覆盖范围有限、可控性和稳定性差,并且无法重现极端危险场景。室内测试可以弥补实际道路测试的不足。本研究建立了基于硬件在环(HIL)的 V2X 相关测试实验室模拟测试框架,以及相关测试用例和测试评估系统。该框架可以测试诸如前向碰撞警告(FCW)、交叉碰撞警告(ICW)等常见应用,以及更高级的功能,如协同自适应巡航控制(CACC)测试和全球导航卫星系统(GNSS)注入测试。根据这项研究,测试结果表明,该框架(CarTest)具有可靠的输出、强大的可重复性、模拟严重危险场景的能力,并且具有高度的可扩展性。同时,为了研究人员的利益,本出版物强调了几个相关的 HIL 挑战和解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0682/9269815/ac499a0d2d7b/sensors-22-05019-g001.jpg

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