CPS: Medium: Safety Assured, Performance Driven Autonomous Vehicles

CPS：中：安全有保证、性能驱动的自动驾驶汽车

• 批准号: 2211599
• 负责人: Mark Campbell
• 金额: $ 119.54万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211599&HistoricalAwards=false
• 关键词: CPS; Medium; Safety; Assured; Performance

Capabilities of autonomous vehicles has surged in the last ten years, propelled by the promise that, in a very near future, commercial self-driving cars will be safe and perform well. Academia is spurring ground-breaking research (e.g., deep learning) and industry is validating software and hardware extensively with millions of miles being driven on the roads and in simulation. Yet, by all accounts - we are still years away from full deployment. One of the primary limitations is the presence of events outside `typical' scenarios. These events range from environmental anomalies (e.g., a swerving car), sensor mistakes (e.g., missed detection of a truck) to security challenges (e.g., remote attacks, spoofing of sensors). These events, while typically rare, reduce reliability of self-driving cars to a level that is unacceptable to the consumer. This research program will develop new algorithms, hardware and validated CPS architecture concepts for autonomous systems operating for long periods of time, such as self-driving cars and flying delivery robots. The work will also be applicable to any autonomous system operating in dynamic environments, such as robots operating in public areas and the home. This research project will develop a holistic CPS architecture for safety assurance and continual performance improvement for autonomous systems operating over long periods of time via probabilistic algorithms, safety guarantees and a secure and agile platform. The technical approach develops three sub-architectures for autonomous CPS systems. A Safety Assured architecture provides probabilistic collision guarantees on secure hardware. A Performance Driven architecture provides robust perception and planning in general conditions, with adaptable algorithms and hardware via dynamic resource allocation. And a Self-Improving architecture works in the background to reason about rare events outside typical scenarios and improve perception and planning algorithms via model learning and software updates. Importantly, by directly working with the inherent coupling between the hardware platform and algorithms, a safety assured CPS architecture will be developed to provide collision avoidance guarantees due to rare events. In addition, adaptive resource allocation on a hardware/software platform along with novel agile algorithms which are adaptable will allow the system to further refine and update inference about the scene and plan options, as well as improve over time. Two experimental testbeds will be used to validate the research. The first is a robot driving in a controlled lab environment in a small-scale city. The second testbed utilizes regularly logged sensor data from a self-driving car to evaluate perception-based mistakes, environmental anomalies, and continual improvement over time.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.

自动驾驶汽车的能力在过去十年中大幅增长，这是因为在不久的将来，商用自动驾驶汽车将安全且性能良好。学术界正在推动突破性的研究（例如，深度学习）和行业正在广泛验证软件和硬件，在道路上和模拟中行驶数百万英里。然而，从各方面来看，我们距离全面部署还有数年时间。主要限制之一是“典型”情景之外的事件。这些事件包括环境异常（例如，突然转向的汽车），传感器错误（例如，卡车的错过检测）到安全挑战（例如，远程攻击、欺骗传感器）。这些事件虽然通常很少发生，但会将自动驾驶汽车的可靠性降低到消费者无法接受的水平。该研究计划将为长时间运行的自主系统开发新的算法，硬件和经过验证的CPS架构概念，例如自动驾驶汽车和飞行交付机器人。这项工作也将适用于在动态环境中运行的任何自主系统，例如在公共区域和家庭中运行的机器人。该研究项目将开发一个整体的CPS架构，通过概率算法，安全保证和安全敏捷的平台，为长时间运行的自主系统提供安全保证和持续的性能改进。该技术方法为自主CPS系统开发了三个子架构。安全保证架构在安全硬件上提供概率冲突保证。性能驱动的架构通过动态资源分配提供可适应的算法和硬件，在一般情况下提供强大的感知和规划。一个自我改进的架构在后台工作，以推理典型场景之外的罕见事件，并通过模型学习和软件更新来改进感知和规划算法。重要的是，通过直接与硬件平台和算法之间的固有耦合一起工作，将开发一个安全保证的CPS架构，以提供由于罕见事件而导致的碰撞避免保证。此外，硬件/软件平台上的自适应资源分配沿着可适应的新颖敏捷算法将允许系统进一步细化和更新关于场景和计划选项的推断，以及随时间改进。两个实验测试台将用于验证研究。第一个是机器人在小规模城市的受控实验室环境中驾驶。第二个测试平台利用自动驾驶汽车定期记录的传感器数据，评估基于感知的错误、环境异常和随时间的持续改进。该奖项反映了NSF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。

项目成果

Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization

• DOI: 10.1109/icra48891.2023.10160298
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Junan Chen;Josephine Monica;Wei-Lun Chao;Mark E. Campbell