Collaborative Research: CPS: Medium: RUI: Cooperative AI Inferencein Vehicular Edge Networks for Advanced Driver-Assistance Systems

在先进的驾驶辅助系统(ADAS)中，人工智能(AI)在提高驾驶安全方面表现出了优越的性能。最新的深度神经网络(DNN)以增加模型复杂性为代价获得高精度，这增加了车载处理单元执行ADAS推理任务的计算负担。该项目的主要目标是开发具有新兴边缘计算范式的创新协作人工智能推理策略。这些策略能够自适应地调整协作推理技术，以最大限度地利用现有的计算和通信资源，最终实现高精度和实时的推理。该项目将促进无线通信、边缘计算、计算机视觉、自动驾驶试验台开发和汽车制造方面的专家之间更多的合作，并促进人工智能在各种物联网系统中的应用。从这个项目开发的教育试验台可以整合到课程中，提供实践体验。该项目将使本科生、硕士和博士项目受益，并通过利用现有的与多样性相关的外展努力，增加代表不足的群体的参与度。来自罗文大学、坦普尔大学、石溪大学和凯特琳大学的多学科团队将组成一个互补的专业团队，以开展协作人工智能推理的协调研究。PI探索综合研究，以实现资源受限的ADAS中的深度学习技术，以实现高精度和实时推理。从理论上讲，PI计划利用DNN可以被分解为一组细粒度组件的观察结果，以允许在车辆端和边缘服务器端进行分布式AI推理，以实现推理加速。在应用方面，PI计划设计新的DNN模型，这些模型针对协作AI推理范式进行了优化。在试验床方面，凯特琳大学通用汽车移动研究中心将开发一个具有V2X通信和边缘计算能力的汽车边缘计算平台。实现了协同AI推理系统，并在真实的车载边缘计算环境中对研究成果进行了验证。由该项目开发的数据、软件和教育试验台将得到广泛传播。自动驾驶试验台开发、智能交通系统和汽车制造领域的专家将从事与项目相关的问题，以确保该项目中的相关挑战对现实世界的应用产生影响。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。

Artificial Intelligence (AI) has shown superior performance in enhancing driving safety in advanced driver-assistance systems (ADAS). State-of-the-art deep neural networks (DNNs) achieve high accuracy at the expense of increased model complexity, which raises the computation burden of onboard processing units of vehicles for ADAS inference tasks. The primary goal of this project is to develop innovative collaborative AI inference strategies with the emerging edge computing paradigm. The strategies can adaptively adjust cooperative inference techniques for best utilizing available computation and communication resources and ultimately enable high-accuracy and real-time inference. The project will inspire greater collaborations between experts in wireless communication, edge computing, computer vision, autonomous driving testbed development, and automotive manufacturing, and facilitate AI applications in a variety of IoT systems. The educational testbed developed from this project can be integrated into courses to provide hands-on experiences. This project will benefit undergraduate, master, and Ph.D. programs and increase under-represented groups’ engagement by leveraging the existing diversity-related outreach efforts.A multi-disciplinary team with complementary expertise from Rowan University, Temple University, Stony Brook University, and Kettering University is assembled to pursue a coordinated study of collaborative AI inference. The PIs explore integrative research to enable deep learning technologies in resource-constrained ADAS for high-accuracy and real-time inference. Theory-wise, the PIs plan to take advantage of the observation that DNNs can be decomposed into a set of fine-grained components to allow distributed AI inference on both the vehicle and edge server sides for inference acceleration. Application-wise, the PIs plan to design novel DNN models which are optimized for the cooperative AI inference paradigm. Testbed-wise, a vehicle edge computing platform with V2X communication and edge computing capability will be developed at Kettering University GM Mobility Research Center. The cooperative AI inference system will be implemented, and the research findings will be validated on realistic vehicular edge computing environments thoroughly. The data, software, and educational testbeds developed from this project will be widely disseminated. Domain experts in autonomous driving testbed development, intelligent transportation systems, and automotive manufacturing will be engaged in project-related issues to ensure relevant challenges in this project are impactful for real-world applications.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.