CRII: RI: Deep neural network pruning for fast and reliable visual detection in self-driving vehicles

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公共法律117-2)。该项目的目标是简化大型通用深度神经网络，以便在自动驾驶车辆中进行快速可靠的视觉检测。该项目开发的紧凑型模型需要更少的计算、能源消耗和二氧化碳排放。此外，这些紧凑型模型的延迟较低，在紧急情况下可以转化为更高的存活率。通过促进自动驾驶汽车的更广泛部署，该项目还将有助于降低与驾驶相关的劳动力成本，并为老年人和残疾人提供更大和更安全的移动性。最后，调查者将把项目中的相关技术和发现引入课堂，并让来自不同背景的学生参与到这项研究中来。这些活动将帮助俄亥俄州和附近的密歇根州的学生为未来的汽车工业做准备。这两个州是美国汽车生产的前两个州。这项研究通过深度网络修剪和对抗性训练，解决了高效和可靠的自动驾驶视觉检测问题。中心假设是在一个大型的通才网络中有许多冗余的、与任务无关的、甚至是干扰的参数/特征。当部署在自动驾驶车辆上时，这些不必要和过度安装的组件将使自动视觉检测变得缓慢，并为恶意攻击打开更多大门。这些组件应该被修剪或改进。该项目有两个具体目标：(1)创建自动驾驶视觉检测的深度网络剪枝框架。该框架将是主动的(而不是在训练之后)，并将利用位置和班级信息，以及(2)提高修剪后的自动驾驶检测器的稳健性。研究人员将研究修剪模型的对抗性稳健性，并通过对抗性训练寻求改进。这项研究可能会导致偏离在各种应用中使用大型通用网络的主导趋势，而是推导出用于自动驾驶汽车高效和可靠的视觉检测的轻量级专家网络。该项目将提供一些实现完全(5级)自动驾驶的技术。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The objective of this project is to simplify large general-purpose deep neural networks for fast and trustworthy visual detection in self-driving vehicles. The project develops compact models that require less computation, energy consumption, and carbon dioxide emissions. In addition, lower latency of these compact models can translate to higher survival rates during emergencies. Through facilitating a wider deployment of self-driving cars, the project will also help reduce driving-related labor costs and provide greater and safe mobility to the elderly and the disabled. Finally, the investigator will introduce relevant technology and findings from the project to the classroom and involve students from diverse backgrounds in this research. These activities will prepare students for tomorrow's automobile industry in Ohio and nearby Michigan, the top two states for auto production in the US.This research addresses problems of efficient and reliable self-driving visual detection through deep network pruning and adversarial training. The central hypothesis is that there are many redundant, task-irrelevant, and even interfering parameters/features in a large generalist network. When deployed on self-driving vehicles, such unnecessary and overfitted components will render automated visual detection slow and open more doors for malign attacks. Such components should be pruned or improved. This project has two specific aims: (1) creating a deep network pruning framework for self-driving visual detection. The framework will be proactive (not after the fact of training) and will utilize both location and class information, and (2) improving the robustness of the pruned self-driving detectors. The investigator will study the adversarial robustness of the pruned models and seek improvement via adversarial training. The research can lead to departing from the dominant trend of employing large generalist networks for various applications to deriving lightweight specialist networks for efficient and reliable visual detection in self-driving cars. The project will provide some technologies towards fully (Level 5) autonomous driving.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.