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

合作研究：中枢神经系统核心：中：未来自动驾驶汽车的网络协作感知

• 批准号: 1956445
• 负责人: Ramesh Govindan
• 金额: $ 90万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956445&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

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.

每年，全球约有135万人死于道路事故。自动驾驶和驾驶员辅助技术可以大大提高车辆的安全性。然而，对于这些技术来说，确保在广泛的异常流量情况下的可靠性仍然是一个关键挑战。为了让社会接受，这些技术必须达到或超过人类驾驶安全水平（例如，死亡之间的1亿英里）。如今，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，车辆的视野可能会被其他车辆阻挡。借助5G和高通Cellular-V2X技术等先进的无线通信技术，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穿透障碍物，这种能力我们称之为网络支持的合作感知。 虽然网络支持的合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。今天的车载通信技术实际上可以达到大约6-10 Mbps，但先进的车载传感器会产生数百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，同时将网络启用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，例如行人，自行车，三轮车，卡车，汽车等，其中并非所有参与者都配备了传感器。该项目将开发抽象，算法和工具，用于大规模的网络支持的合作感知，建立在称为一瞥的抽象基础上，这是车辆传感器视图的一部分的处理表示。一瞥可以表示视图中的单个对象，也可以表示3D空间中的网格，并且可以以不同的粒度表示，以权衡细节的带宽。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何时需要哪些瞥见表示的方法;在尊重信道容量限制的同时协调这些瞥见到车辆的传输的调度算法;训练机器学习模型的方法，以使用瞥见增强的复合视图做出控制决策;以及确保对一瞥中毒的鲁棒性的技术。除了由网络支持的合作感知实现的可靠自动驾驶技术所带来的社会优势之外，该项目还将把研究结果纳入课程，参与者将指导本科生，并为更广泛地参与计算做出贡献，特别是寻求让来自中南洛杉矶Crenshaw地区代表性不足的群体的学生，和学生在蒙特贝洛联合学区的令人兴奋的主题计算。与通用汽车的合作将简化技术转让的道路，并将使博士生接触与汽车系统相关的主题。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

AutoCast: scalable infrastructure-less cooperative perception for distributed collaborative driving

AutoCast：可扩展的无基础设施的协作感知，用于分布式协作驾驶

• DOI: 10.1145/3498361.3538925
• 发表时间: 2022
• 期刊: ACM MobiSys
• 作者: Qiu, Hang;Huang, Po-Han;Asavisanu, Namo;Liu, Xiaochen;Psounis, Konstantinos;Govindan, Ramesh
• 通讯作者: Govindan, Ramesh