Collaborative Research: CNS Core: Medium: Network-Enabled Cooperative Perception for Future Autonomous Vehicles

每年，全球约有135万人死于道路交通事故。自动驾驶和驾驶员辅助技术可以极大地提高车辆的安全性。然而，对于这些技术来说，确保在一系列不寻常的交通情况下的可靠性仍然是一个关键挑战。为了被社会接受，这些技术必须达到或超过人类驾驶安全水平(例如，死亡间隔1亿英里)。今天，每辆自动驾驶汽车都依靠自己的传感器来感知环境并做出独立的驾驶决策。不幸的是，这些传感器依赖于视线感知，车辆的视线可能会被其他车辆挡住。有了先进的无线通信技术，如5G和高通的Ccell-V2X技术，车辆将能够直接或使用路边基础设施与其他车辆共享传感器数据，从而使车辆能够有效地看到障碍物，这种能力我们称为网络使能的协作感知。虽然支持网络的协作感知是一项引人注目的技术，但网络仍然是一个根本的瓶颈。今天的车辆通信技术在实践中可以达到大约6-10 Mbps，但先进的车辆传感器产生数百Mbps的原始数据。该项目旨在解决原始传感器数据的丰富性和网络瓶颈之间的紧张关系，同时将网络支持的协作感知扩展到具有多模式交通的极其密集的交通情况，如行人、自行车、三轮车、卡车、汽车等，其中并不是所有参与者都可能配备传感器。该项目将开发抽象、算法和工具，用于大规模的网络使能协作感知，建立在被称为掠影的抽象基础上，这是对车辆传感器视图的一部分的处理表示。掠影可以表示视图中的单个对象，也可以表示3D空间中的栅格，并且可以以不同的粒度表示，以牺牲带宽来换取细节。鉴于这种抽象，该项目将开发：在复杂和高度动态的交通环境中，确定哪些车辆需要哪些扫视表示以及何时需要扫视表示的方法；在尊重信道容量限制的同时协调这些扫视到车辆的传输的调度算法；使用增强的扫视增强的复合视图训练机器学习模型以做出控制决策的方法；以及确保对瞥见中毒的稳健性的技术。除了可靠的自动驾驶技术带来的社会优势外，该项目还将把研究成果纳入课程，参与者将指导本科生，并为更广泛地参与计算做出贡献，特别是寻求让洛杉矶中南部克伦肖地区代表不足的群体的学生以及蒙特贝洛联合学区的学生接触令人兴奋的计算主题。与通用汽车的合作将为技术转让铺平道路，并将使博士生接触到与汽车系统相关的主题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Annually, road accidents contribute to approximately 1.35 million fatalities globally. Automated driving and driver assist technology can greatly increase vehicular safety. However, for these technologies, ensuring dependability over a broad set of unusual traffic situations remains a key challenge. To be socially acceptable, these technologies must match or exceed human driving safety levels (e.g. 100 million miles between fatalities). Today, each autonomous vehicle relies on its own sensors to perceive the environment and make independent driving decisions. Unfortunately, these sensors rely on line-of-sight perception and a vehicle's view can be blocked by other vehicles. With advanced wireless communication technologies such as 5G and Qualcomm's Cellular-V2X Technologies, vehicles will be able to share their sensor data with other vehicles, either directly or using roadside infrastructure, so that vehicles can effectively see through obstacles, a capability we call network-enabled cooperative perception. While network-enabled cooperative perception is a compelling technology, the network remains a fundamental bottleneck. Today's vehicular communication technologies can, in practice, achieve about 6-10 Mbps, but advanced vehicular sensors generate hundreds of Mbps of raw data. This project seeks to resolve the tension between the richness of the raw sensor data and the network bottleneck, while scaling network-enabled cooperative perception to extremely dense traffic situations with multi-modal traffic such as pedestrians, bicycles, three-wheelers, trucks, cars etc. in which not all participants may be sensor-equipped. The project will develop abstractions, algorithms, and tools for network-enabled cooperative perception at scale, building upon an abstraction called a glimpse, which is a processed representation of a part of a vehicle's sensor view. Glimpses can represent individual objects within the view, or a grid in 3D space, and can be represented at different granularities that trade-off bandwidth for detail. Given this abstraction, the project will develop: methods to determine, in a complex and highly dynamic traffic setting, which glimpse representations are needed for which vehicles and by when; scheduling algorithms to coordinate the transmission of these glimpses to vehicles while respecting channel capacity constraints; methods to train machine learning models to make control decisions using glimpse-enhanced composite views; and techniques to ensure robustness to glimpse poisoning. Beyond the societal advantages resulting from reliable autonomous driving technology as enabled by network-enabled cooperative perception, the project will incorporate the results of the research into curricula, participants will mentor undergraduates and contribute to efforts to broader participation in computing, specifically seeking to expose students from under-represented groups in the Crenshaw area of South-Central Los Angeles, and students in the Montebello Unified School district to exciting topics in computing. Collaboration with General Motors will ease the path towards technology transfer and will expose PhD students to topics relevant to automotive systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.