FMitF: Collaborative Research: Track I: Predictive Online Safety Analysis from Multi-hop State Estimates for High-autonomy on Highways

FMITF：合作研究：第一轨：通过多跳状态估计进行预测在线安全分析，以实现高速公路的高度自治

• 批准号: 1918531
• 负责人: Sayan Mitra
• 金额: $ 48.95万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918531&HistoricalAwards=false
• 关键词: FMitF; Collaborative; Research; Track; Predictive

The goal of this project is to bring safety assurance to autonomous and semi-autonomous vehicles. The approach is to lengthen the time that a car can predict its driving path, and share this path with surrounding vehicles. With these expanded predictions, it is possible to estimate the current and future behaviors of vehicles, according to their design models. Currently, online formal safety analysis can promise guarantees and oversight, but overly conservative approaches can lead to bad driving. This is in contrast to the use of test-driving data and machine learning to build driving models, which are difficult to analyze. The project aims to discover the right balance by computationally (1) estimating the current state of the autonomous vehicle and its multi-hop environment from sensor data, (2) predicting vehicle trajectories 4-6 seconds into future, and (3) checking the models and predictions---all in milliseconds. A new scientific workshop will be created to explore similar issues in autonomy, in addition to a new undergraduate course on autonomy.The project aims to deliver (1) new sensor-fusion algorithms over Vehicle-to-Infrastructure/Vehicle (V2X) systems, (2) a first-of-its-kind open, machine-interpretable library of agent models for driving predictions, (3) algorithms for model identification, and (4) algorithms for checking safety online. These modules will be integrated in an end-to-end system --- OmniVisor --- and evaluated in realistic accident-prone scenarios with real vehicles in University of Michigan's Mcity facility. The research will build connections across the disciplines of formal methods, hybrid dynamical systems, estimation and detection theory, and mobile networking. If successful OmniVisor will provide a scientific basis for obtaining safety assurances for vehicles in mixed-autonomy scenarios and experimentally demonstrate the approach on the road with real vehicles.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.

该项目的目的是为自动驾驶和半自治的车辆带来安全保证。方法是延长汽车可以预测其驾驶道路的时间，并与周围的车辆共享这条路径。随着这些扩展的预测，可以根据其设计模型来估计车辆的当前和未来行为。当前，在线正式安全分析可以保证保证和监督，但是过于保守的方法可能导致不良驾驶。这与使用测试驾驶数据和机器学习来构建驾驶模型相反，这很难分析。该项目旨在通过计算（1）从传感器数据估算自动驾驶汽车及其多跳环境的当前状态，（2）预测未来4-6秒的车辆轨迹，以及（3）检查模型和预测 - 全部以毫秒为单位。除了在自主权的新本科课程外，还将创建一个新的科学研讨会，以探索自主权的类似问题。该项目旨在交付（1）新的传感器融合算法对车辆到基础结构/车辆（V2X）系统的新传感器 - 融合算法，（2）首次开放，机器的识别库和模型的模型（3），（3）在线检查安全性的算法。这些模块将集成到端到端系统中---杂种 - - 在密歇根大学的MCITY设施中与实际车辆进行现实事故的情况进行评估。该研究将建立在形式方法，混合动力学系统，估计和检测理论以及移动网络的学科之间的联系。如果成功的杂种者将为在混合自治方案中获得车辆的安全保证提供科学基础，并在实验上证明了使用真实车辆的道路上的方法。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。

项目成果

Voice localization using nearby wall reflections

• DOI: 10.1145/3372224.3380884
• 发表时间: 2020-04
• 期刊: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking
• 作者: Sheng Shen

Planning in Dynamic and Partially Unknown Environments

• DOI: 10.1016/j.ifacol.2021.08.493
• 发表时间: 2021
• 作者: Kristina Miller;Chuchu Fan;S. Mitra

???????????? : Boosting Scenario Verification Using Symmetry Abstractions

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• 发表时间: 2021
• 期刊: Cham
• 作者: Hussein Sibai;Yangge Li;Sayan Mitra
• 通讯作者: Sayan Mitra

Symmetry for Boosting Algorithmic Proofs of Cyberphysical Systems

对称性促进网络物理系统的算法证明

• DOI: 10.1109/mc.2022.3190954
• 发表时间: 2022
• 期刊: Computer
• 影响因子: 2.2
• 作者: Mitra, Sayan;Sibai, Hussein
• 通讯作者: Sibai, Hussein

Egocentric abstractions for modeling and safety verification of distributed cyber-physical systems

用于分布式网络物理系统建模和安全验证的以自我为中心的抽象

• DOI: 10.1109/spw53761.2021.00046
• 发表时间: 2021
• 期刊: 2021 IEEE Security and Privacy Workshops (SPW
• 作者: Jeon, Sung Woo;Mitra, Sayan
• 通讯作者: Mitra, Sayan