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

人工智能（AI）在提高高级驾驶辅助系统（ADAS）的驾驶安全性方面表现出了卓越的性能。目前最先进的深度神经网络（dnn）以增加模型复杂性为代价来实现高精度，这增加了车载处理单元用于ADAS推理任务的计算负担。该项目的主要目标是利用新兴的边缘计算范式开发创新的协作人工智能推理策略。该策略可以自适应调整协作推理技术，以最佳地利用可用的计算和通信资源，最终实现高精度和实时推理。该项目将激发无线通信、边缘计算、计算机视觉、自动驾驶试验台开发和汽车制造领域专家之间的更大合作，并促进人工智能在各种物联网系统中的应用。从该项目开发的教育测试平台可以集成到课程中，以提供实践经验。该项目将惠及本科生、硕士和博士项目，并通过利用现有的与多样性相关的外展工作，增加代表性不足的群体的参与。一个由罗文大学、天普大学、石溪大学和凯特林大学组成的多学科团队将进行协作人工智能推理的协调研究。pi探索综合研究，使深度学习技术在资源受限的ADAS中实现高精度和实时推理。从理论上讲，pi计划利用dnn可以分解为一组细粒度组件的观察结果，从而允许在车辆和边缘服务器端进行分布式AI推理，以进行推理加速。在应用方面，pi计划设计针对协作AI推理范式进行优化的新型深度神经网络模型。在测试平台方面，将在凯特林大学通用汽车移动研究中心开发具有V2X通信和边缘计算能力的车辆边缘计算平台。实现协同AI推理系统，并在现实的车载边缘计算环境中对研究成果进行全面验证。从这个项目开发的数据、软件和教育测试平台将被广泛传播。自动驾驶测试平台开发、智能交通系统和汽车制造领域的专家将参与项目相关问题，以确保项目中的相关挑战对实际应用产生影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.