CPS: Synergy: TTP Option: Anytime Visual Scene Understanding for Heterogeneous and Distributed Cyber-Physical Systems

尽管汽车自动化取得了许多进步，但仍有许多工作要做：目前最好的自动驾驶汽车仍落后于人类驾驶员，而联网汽车（V2V）和基础设施（V2I）标准才刚刚出现。为了使这样的网络物理系统充分发挥其潜力，它们必须能够利用人类最丰富和最复杂的能力之一，我们认为这是理所当然的：看到和理解视觉世界。如果自动驾驶汽车拥有这种能力，它们就可以更智能地驾驶，并共享有关道路和环境条件、事件和异常的信息，以提高其他自动驾驶汽车和人类驾驶员的态势感知和安全性。这就是该项目的目标，实现计算机视觉、机器学习和网络物理系统之间的协同作用，从而实现更安全、更便宜和更智能的交通运输部门，并有潜在的应用于其他部门，包括农业、食品质量控制和环境监测。为了实现这一目标，该项目汇集了计算机视觉、传感、嵌入式计算、机器学习、大数据分析和传感器网络方面的专业知识，开发了一个集成的边缘云架构，用于(1)“随时场景理解”，统一计算机视觉中的各种场景理解方法；(2)“协作场景理解”，利用车对车和车对基础设施协议与多个系统进行协调。同时(3)强调如何在不影响整体服务质量的情况下大规模管理安全和隐私。该架构可用于自动驾驶和驾驶员辅助系统，并可嵌入基础设施（数字标志，交通信号灯）中，以避免交通拥堵，降低堆积风险并提高态势感知能力。通过与匹兹堡市公共工程部门车辆、卡内基梅隆大学NAVLAB自动驾驶车辆以及匹兹堡正在开发的智能道路基础设施走廊的整合，验证并将研究成果转化为实践。该项目还包括促进新网络物理系统劳动力发展的活动，包括学生参与研究、共同教授多学科课程和共同组织研讨会。

Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world. If automated vehicles had this ability, they could drive more intelligently, and share information about road and environment conditions, events, and anomalies to improve situational awareness and safety for other automated vehicles as well as human drivers. That is the goal of this project, to achieve a synergy between computer vision, machine learning and cyber-physical systems that leads to a safer, cheaper and smarter transportation sector, and which has potential applications to other sectors including agriculture, food quality control and environment monitoring.To achieve this goal, this project brings together expertise in computer vision, sensing, embedded computing, machine learning, big data analytics and sensor networks to develop an integrated edge-cloud architecture for (1) "anytime scene understanding" to unify diverse scene understanding methods in computer vision, and (2) "cooperative scene understanding" that leverages vehicle-to-vehicle and vehicle-to-infrastructure protocols to coordinate with multiple systems, while (3) emphasizing how security and privacy should be managed at scale without impacting overall quality-of-service. This architecture can be used for autonomous driving and driver-assist systems, and can be embedded within infrastructure (digital signs, traffic lights) to avoid traffic congestion, reduce risk of pile-ups and improve situational awareness. Validation and transition of the research to practice are through integration within City of Pittsburgh public works department vehicles, Carnegie Mellon University NAVLAB autonomous vehicles, and across the smart road infrastructure corridor under development in Pittsburgh. The project also includes activities to foster development of a new cyber-physical systems workforce, though involvement of students in the research, co-taught multi-disciplinary courses, and co-organized workshops.