CPS: Medium: Collaborative Research: Collective Intelligence for Proactive Autonomous Driving (CI-PAD)

该项目的目标是开发通过车辆对车辆(V2V)和车辆对网络(V2X)共享的实时态势感知。方法是将传感器的感知与对其情况的解释相结合，以实现更安全的决策，并考虑到车辆和基础设施之间通信的限制。支持自动驾驶和自动驾驶车辆的高速公路系统将包括基础设施传感器和车载车辆传感器，它们之间将实现大规模连接，并在整个交通网络中实现分布式智能。由此产生的集体智能是一种自动驾驶车辆充当移动传感器，与固定基础设施传感器一起相互增强，以构建实时交通图景。这一实时画面用于开发主动驾驶行动，以优化交通流量并将事故风险降至最低。更广泛的影响包括对对需要研究生培训的职业感兴趣的本科生进行重点指导，以扩大他们在计算机和工程领域的参与。研究人员组织了一个跨学科项目，涉及信号处理和机器学习、控制和优化、通信和网络科学。主动驾驶的集体智能框架包括以下模块：1)场景构建，包括信号处理和机器学习，用于从多模式多视角传感器构建驾驶环境的表示；2)情景解释，包括渐进级别的驾驶环境动态分析；3)决策，包括支持主动驾驶的安全和优化流量的优化和控制；和4)故障安全网络，由通信和网络科学组成，支持在名义感知和通信条件下优化流量，以及在受损的感知和通信条件下缓和流量。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。

The aim of this project is to develop real-time situational awareness that is shared via vehicle-to-vehicle (V2V) and vehicle-to-network (V2X). The approach is to combine the perception of sensors with interpretation of their situation to enable safer decisions, and take into account the limitations of the communication between vehicles and infrastructure. A highway system that supports autonomous and self-driven vehicles will include infrastructure sensors and onboard vehicle sensors, with massive connectivity among them and distributed intelligence across the entire transportation network. The resulting collective intelligence is one where autonomous vehicles serve as mobile sensors that augment one another along with fixed infrastructure sensors, to construct a real-time picture of traffic. This real-time picture is used to develop proactive driving actions that optimize traffic flow and minimize accident risk. The broader impacts include focused mentoring of undergraduate students who are interested in careers that require graduate training, to broaden participation in the fields of computing and engineering.The researchers organize an interdisciplinary project in signal processing and machine learning, control and optimization, communication and network science. The collective intelligence framework for proactive driving includes the following modules: 1) Scene Construction, consisting of signal processing and machine learning for constructing a representation of the driving environment from multi-modal multi-view sensors; 2) Situational Interpretation, consisting of driving environment dynamic analysis at progressive levels; 3) Decision Making, consisting of optimization and control to support proactive driving for safety and optimized flow; and 4) A Failsafe Network, consisting of communication and network science that supports optimized traffic flow under nominal conditions of sensing and communication, and moderated flow under conditions of compromised sensing and communication.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.