S&AS:FND:COLLAB:Unsupervised Rare Event Learning - With Applications on Autonomous Vehicles

智能物理系统(IPS)架构必须是可识别的、可执行任务的、自适应的和符合道德的，尤其是当用户需要可靠地处理不确定的、动态变化的情况时。尽管收集和获得了大量关于IPS的数据，但自动驾驶汽车(AV)行业面临的一个重大挑战是有效预测和避免罕见但灾难性的故障事件的能力。开发一种信息结构，使用户能够最大限度地利用海量数据，并对危急情况的复杂性和稀缺性进行建模，将有助于应对这一挑战。本项目研究了一种新的、无监督的罕见事件学习框架，用于反病毒研究，该框架利用收集的数据来评估和缓解IPSS的风险、限制和故障模式。该框架结合了通过无监督学习算法对高维时间序列驱动数据建模的灵活性，以及罕见事件分析的统计稳健性和计算效率。研究的具体目标包括：1)建立一个从驾驶数据库中提取安全关键信息的有效框架；2)为无人机驾驶环境中罕见但不利的事件制定可靠的概率测量方案；3)将框架和研究成果传播给迫切需要可靠评估和开发方法的行业和政府单位；以及4)在罕见事件估计学术界和IPSS研究之间交叉促进研究推力。这项研究将融合从数据中学习的高保真驾驶表示和统计上严格的罕见事件分析，以便在模拟复杂驾驶情况下进行AV设计评估将变得高效。它将机器学习与模拟方法相结合，包括罕见事件分析、随机优化和粒子方法，以提高最先进算法的性能。该项目将通过在顶级期刊、会议、教程和特邀研讨会上传播成果，促进罕见事件估计(运筹学)和机器学习/机器人学(IPSS)研究人员之间的变革性学术交流。这项研究将在PIs的机构中培训未被充分代表的少数族裔学生，提供与主要的反病毒学术和行业参与者见面的机会，并向他们展示如何应用数学和工程技能来提高交通机动性、安全性和道路效率。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。

Intelligent physical systems (IPSs) architecture must be cognizant, taskable, adaptive, and ethical, particularly when users need to reliably address uncertain, dynamically changing situations. Despite the collection and availability of massive data on IPSs, a significant challenge, faced by the autonomous vehicle (AV) industry is the capability to efficiently anticipate and avoid rare but catastrophic failure events. Developing an information structure that enables users to make the best use of massive data and to model the complexity and rarity of critical situations would contribute to addressing this challenge. This project investigates a novel, unsupervised rare-event learning framework for AV research that utilizes the collected data to assess and alleviate the risks, limitations, and failure modes of IPSs. The framework combines the flexibility in modeling high-dimensional time-series driving data via unsupervised learning algorithms, with the statistical robustness and computational efficiency from rare-event analysis. The specific goals of the research include: 1) formulate an effective framework to extract safety-critical information from driving database; 2) develop a solid probabilistic measurement scheme for rare but adverse events in AV driving contexts; 3) disseminate the framework and research outcomes to industry and government units in urgent need of reliable evaluation and development methods; and 4) cross-fertilize research thrusts between the academic communities of rare-event estimation and the study of IPSs. The research will merge high-fidelity driving representations learned from data and statistically rigorous rare event analysis so that AV design evaluation in simulated complex driving situations will become highly efficient. It will synthesize machine learning with simulation methodologies, including rare-event analysis, stochastic optimization, and particle methods to boost the performances of state-of-the-art algorithms. The project will foster transformative academic cross-fertilization between researchers in rare-event estimation (operations research) and IPSs (machine learning/robotics), by disseminating the outcomes in top-tier journals, conferences, tutorials and invited seminars. The research will train under-represented minority students at the PIs' institutions, provide opportunities to meet key AV academic and industry players, and show them how to apply mathematical and engineering skills to improve transportation mobility, safety, and roadway efficiency.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.