CPS: Medium: Safety Assured, Performance Driven Autonomous Vehicles

在不久的将来，商用自动驾驶汽车将安全且性能良好，这一承诺推动了自动驾驶汽车的能力在过去十年中大幅提升。学术界正在推动突破性的研究（例如深度学习），工业界正在广泛验证软件和硬件，在道路上和模拟中行驶了数百万英里。然而，所有人都说，我们距离全面部署还需要数年时间。其中一个主要限制是“典型”场景之外的事件。这些事件的范围从环境异常（例如，汽车转向），传感器错误（例如，未检测到卡车）到安全挑战（例如，远程攻击，欺骗传感器）。这些事件虽然很少发生，但会将自动驾驶汽车的可靠性降低到消费者无法接受的程度。该研究项目将为自动驾驶汽车和飞行送货机器人等长期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。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平台，为长时间运行的自主系统提供安全保证和持续性能改进。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。性能驱动的体系结构在一般情况下提供健壮的感知和规划，通过动态资源分配提供可适应的算法和硬件。自我改进架构在后台工作，对典型场景之外的罕见事件进行推理，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，通过直接处理硬件平台和算法之间的固有耦合，将开发出一种安全保证的CPS架构，以提供由于罕见事件而避免碰撞的保证。此外，在硬件/软件平台上的自适应资源分配以及具有适应性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，并随着时间的推移而改进。两个实验试验台将用于验证研究结果。第一个是机器人在一个小型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来评估基于感知的错误、环境异常以及随着时间的推移的持续改进。该奖项反映了美国国家科学基金会的法定使命，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。

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.