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

近年来，无人驾驶汽车得到了大力发展。然而，安全探测隐藏物体仍然是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，比如摄像头和激光测距仪，只能探测到它们直接视野内的物体。因此，它们对由于遮挡或恶劣天气而隐藏的物体视而不见。该提案旨在利用无线通信来克服这一挑战，并补充自动驾驶汽车上的传感器。与可见光不同，无线信号可以穿透墙壁和障碍物。该提案提出了一种新的商用无线无线电系统，可以检测和定位自动驾驶汽车周围的物体。它通过开发算法来分析无线信号的组成部分，这些信号会反射出环境中不同的物体。它研究观察到的跨频率信号值，以发现障碍物的材料。这使得系统能够区分隐藏的行人和隐藏的汽车，这应该会引起无人驾驶汽车的不同反应。该研究提出了一种新的框架，可以在室内和室外环境中感知自动驾驶汽车附近障碍物的位置和材料。该公司将开发端到端系统，用于处理自动驾驶汽车上商用无线无线电的无线信号，该系统可以很容易地与自动路径规划系统集成。其主要智力贡献包括：(1)在车载平台上同步商用Wi-Fi/DSRC无线电的新方法，以模拟具有宽带宽的大型天线阵列；(2)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)一种算法，通过研究跨频率反射的信号来发现感兴趣物体的材料。该系统将在机器人和车辆平台上进行室内和室外的全面验证。这项提议的研究解决了自动驾驶汽车的一个基本限制：它们无法看到视野中被遮挡的物体。考虑到仅在美国每年就有80多万起与盲点相关的事故，它对驾驶安全的影响可能是革命性的。卡耐基梅隆大学一门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。动手无线实验室将被开发为匹兹堡地区高中生的Spark周六拓展计划的一部分。拟议的研究将通过同行评议会议、讲习班和期刊传播。

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.