CRII: NeTS: A Wireless Sensing Platform for Safe Autonomous Driving

近年来，人们积极推动制造无人驾驶汽车。然而，安全地检测隐藏的物体仍然是完全自动驾驶面临的主要挑战。如今，自动驾驶汽车上的传感器，如摄像头和激光测距仪，只能检测到它们直接视场中的物体。因此，他们对因遮挡或恶劣天气而隐藏的物体视而不见。这项提议旨在利用无线通信来克服这一挑战，并补充自动驾驶车辆上的传感器。它依赖于这样一个事实：与可见光不同，无线信号可以穿透墙壁和障碍物。该提案提出了一种用于商用无线无线电的新型系统，该系统可以检测和定位自动驾驶车辆周围的物体。它通过开发算法来分析无线信号的成分，这些成分反映了环境中的不同对象。它研究不同频率的观测信号值，以发现构成障碍物的材料。这使得该系统能够区分隐藏的行人和隐藏的汽车，这应该会引起与无人驾驶汽车不同的反应。拟议的研究调查了一种新的框架，可以在室内和室外环境中感知自动驾驶汽车附近的障碍物的位置和材料。它将开发一个端到端系统，以处理自动驾驶车辆上的商用无线无线电上的无线信号，该系统可以很容易地与自动路径规划系统集成。它的主要智能贡献包括：(1)一种新的方法来同步车载平台上的商用Wi-Fi/DSRC无线电，以模拟大型宽带天线阵；(2)一种利用这些信息来定位周围物体的空间方向和距离的机制；(3)一种通过研究跨频率反射的信号来发现感兴趣物体的材料的算法。该系统将在室内和室外机器人和车载平台上进行充分验证。这项拟议的研究解决了自动驾驶汽车的一个根本限制：它们不能看到被遮挡在视线之外的物体。考虑到仅在美国每年就有80多万起与盲点有关的事故，它对驾驶安全的影响可能会发生革命性的变化。卡内基梅隆大学关于无线网络的新研究生课程的课程将纳入这项研究的结果。实践无线实验室将被开发为匹兹堡地区高中生火花星期六推广计划的一部分。拟议的研究将通过同行评议的会议、讲习班和期刊进行传播。

Recent years have witnessed an aggressive push towards building driverless cars. Yet, safely detecting hidden objects remains a major challenge facing fully autonomous driving. Sensors on an autonomous car today, such as cameras and laser range finders, can only detect objects in their direct field-of-view. As a result, they are blind to objects that are hidden due to occlusions or inclement weather. This proposal aims to use wireless communication to overcome this challenge and complement sensors on autonomous vehicles. It relies on the fact that wireless signals, unlike visible light, can penetrate through walls and obstacles. The proposal presents novel system for commodity wireless radios that detects and locates objects around an autonomous vehicle. It does so by developing algorithms that analyze the components of wireless signals that reflect off different objects in the environment. It studies the observed signal values across frequency to discover the material that the obstacles are made of. This allows the system to distinguish between, say, a hidden pedestrian and a hidden car, which should elicit different responses from the driverless car.The proposed research investigates a novel framework to sense the location and material of obstacles in the vicinity of autonomous vehicles in both indoor and outdoor settings. It will develop an end-to-end system to process wireless signals on commodity wireless radios on autonomous vehicles that can readily be integrated with autonomous path planning systems. Its key intellectual contributions include: (1) A novel method to synchronize commodity Wi-Fi/DSRC radios on vehicular platforms to emulate large antenna arrays with wide bandwidth; (2) A mechanism to use this information to locate the spatial direction and range of surrounding objects; (3) An algorithm to discover the material of objects of interest by studying signals reflected off them across frequencies. The system will be fully validated indoors and outdoors on robotic and vehicular platforms. The proposed research tackles a fundamental limitation of autonomous vehicles: they cannot view objects occluded from view. Its impact on the safety of driving is potentially transformative given the annual 800,000+ blind-spot related accidents in the U.S. alone. Curriculum for a new graduate course on wireless networks at Carnegie Mellon will incorporate the findings of this research. Hands-on wireless labs will be developed as a part of the Spark Saturday outreach program for high school students in the Pittsburgh area. The proposed research will be disseminated via peer-reviewed conferences, workshops and journals.