Collaborative Research: OAC Core: Stochastic Simulation Platform for Assessing Safety Performance of Autonomous Vehicles in Winter Seasons

合作研究：OAC Core：用于评估冬季自动驾驶汽车安全性能的随机仿真平台

• 批准号: 2106965
• 负责人: Xiaobai Liu
• 金额: $ 19.97万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106965&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Stochastic

The safety of an autonomous vehicle (AV) highly depends on the generalization capability of its automation systems (e.g., perception and decision-making) when being deployed in diverse physical environments. Although the current commercialization of AVs has been shown to improve traffic safety, AV safety performance under adverse driving conditions in winter seasons still lacks comprehensive evaluation. To bridge the research gap, this project aims to develop a stochastic simulation platform, which examines the efficiency, reliability, and safety of AVs, to prevent costly mistakes in widespread field implementations. The research methods use a foundation of machine learning and physics principles to formulate an integrated and hybrid approach to model stochastic vehicle behaviors in traffic streams. Potential AV safety risks under adverse driving conditions will be assessed with dynamic modeling of vehicle behavior. The project will produce an open-source and cloud-based simulation platform that allows public access to test vehicle automation systems. The simulation models can be improved over time through the use of an online machine learning architecture. The research activities will be closely integrated with a set of education and outreach activities that include (i) incorporating advanced computational techniques into the curriculum, (ii) sparking the interests of younger generations in science and engineering by local K-12 outreach efforts and summer camps, and (iii) broadening the participation of underrepresented student groups in computing through the artificial intelligence club at San Diego State University, a Hispanic serving institution. This multidisciplinary research project aims at contributing improved algorithms in simulation and fundamental knowledge in computing to building an advanced cyberinfrastructure toolkit. The project focuses on producing a stochastic simulation platform that can evaluate the capabilities of AVs' automated driving systems. The motivation is to produce a reliable tool that can model stochastic vehicle behaviors, study vehicle dynamics, and predict potential AV safety risks under adverse driving conditions in winter. To this end, the project will first leverage the physics principles of a microscopic traffic model to regularize the machine learning process for simulating vehicle interactions. Second, both multi-vehicle and single-vehicle crash probabilities in mixed traffic will be predicted by integrating the traffic simulation model with a new vehicle dynamics model. The stochastic vehicle motions will then be studied to assess AV safety performance on icy/snowy pavement. Third, the models will be integrated into an open-source software package with comprehensive documentation and multiple application cases. The expected deliverable will be a public cloud-based platform that is easy to access and is capable of incorporating new data streams for model improvement. After validating the models with field data, the project will connect the simulations with existing automated driving systems for testing. The project can have broad impacts on other science and engineering fields, such as physics-supported artificial intelligence, smart and autonomous systems, and other research domains that depend on simulated data.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.

自动驾驶汽车（AV）的安全性高度取决于其自动化系统（例如，感知和决策）的概括能力，当时在各种物理环境中部署时。尽管目前已经显示出AV的商业化可以提高交通安全性，但是在冬季不良驾驶条件下，AV安全性能仍然缺乏全面的评估。为了弥合研究差距，该项目旨在开发一个随机模拟平台，该平台研究了AV的效率，可靠性和安全性，以防止广泛的现场实现中的昂贵错误。研究方法使用机器学习和物理原理的基础来制定一种集成和混合方法，以模拟交通流中的随机车辆行为。在不利驾驶条件下的潜在AV安全风险将通过车辆行为的动态建模来评估。该项目将产生一个开源和基于云的仿真平台，允许公众访问测试车辆自动化系统。通过使用在线机器学习体系结构，可以随着时间的推移改进仿真模型。研究活动将与一系列教育和外展活动紧密相结合，包括（i）将先进的计算技术纳入课程中，（ii）通过本地K-112宣传工作和夏令营激发年轻一代在科学和工程中的利益，以及（iii）通过不足的学生团体通过计算机群的参与人的人工智能俱乐部，将其参与到San Diego大学，并在San Diego大学中宣布一家迪格戈大学的一家纪念俱乐部。 这个多学科研究项目旨在为构建高级网络基础设施工具包的模拟和基本知识中的改进算法。该项目着重于生产一个随机模拟平台，该平台可以评估AVS自动驾驶系统的功能。动机是生产可靠的工具，该工具可以对随机车辆的行为进行建模，研究车辆动力学，并预测冬季不良驾驶条件下潜在的AV安全风险。为此，该项目将首先利用显微镜交通模型的物理原理，以使模拟车辆相互作用的机器学习过程正常。其次，通过将交通模拟模型与新车辆动力学模型集成在一起，可以预测混合流量中的多车辆和单车崩溃概率。然后将研究随机车辆的运动，以评估冰冷/雪路面上的AV安全性能。第三，这些模型将集成到具有全面文档和多个应用程序案例的开源软件包中。预期的可交付方式将是一个基于公共云的平台，易于访问，并能够合并新的数据流以进行改进。通过字段数据验证模型后，该项目将将模拟与现有的自动驾驶系统进行测试。该项目可能会对其他科学和工程领域产生广泛的影响，例如物理支持的人工智能，智能和自主系统，以及其他依赖模拟数据的研究领域。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查审查标准来通过评估来通过评估来支持的。