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

近年来，无人驾驶汽车的制造受到了积极的推动。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，只能检测其直接视野内的物体。因此，他们对由于遮挡或恶劣天气而隐藏的物体视而不见。该提案旨在使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。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。该提案提出了用于商品无线电的新颖系统，其检测和定位自主车辆周围的物体。它通过开发算法来分析反射到环境中不同物体的无线信号的成分。它研究整个频率的观测信号值，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。拟议的研究调查了一种新的框架，以感测室内和室外环境中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，用于处理自动驾驶汽车上商品无线电的无线信号，该系统可以很容易地与自动路径规划系统集成。其主要的智力贡献包括：（1）一种新的方法，用于同步车载平台上的商用Wi-Fi/DSRC无线电，以模拟具有宽带宽的大型天线阵列;（2）一种使用此信息来定位周围物体的空间方向和范围的机制;（3）一种通过研究跨频率反射的信号来发现感兴趣物体材料的算法。该系统将在室内和室外的机器人和车辆平台上进行全面验证。 拟议的研究解决了自动驾驶汽车的一个基本限制：它们无法查看被遮挡的物体。它对驾驶安全的影响可能是变革性的，因为仅在美国每年就有80多万起与盲点相关的事故。卡耐基梅隆大学的一门新的无线网络研究生课程将纳入这项研究的结果。实践无线实验室将作为匹兹堡地区高中生星火星期六推广计划的一部分开发。拟议的研究将通过同行审查的会议、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.