CAREER: Safe and Agile Autonomous Cyber-Physical Systems

职业：安全、敏捷的自主网络物理系统

• 批准号: 2046582
• 负责人: Madhur Behl
• 金额: $ 54.68万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046582&HistoricalAwards=false
• 关键词: CAREER; Safe; Agile; Autonomous; Cyber

Autonomous Cyber-Physical Systems (CPS), such as self-driving cars, and drones, powered by deep learning and AI based perception, planning, and control algorithms, are forming the basis for significant pieces of our nation’s critical infrastructure, and present direct, and urgent safety-critical challenges. A major limitation with current approaches towards deploying autonomous CPS is in ensuring that the system operates safely, and reliably in situations that do not happen very often under normal operating conditions and are therefore difficult to gather data on. For instance, a self-driving car trained to follow the ‘rules of the road’ will perform well most of the time, but it is the unusual conditions, the edge cases, which pose the hardest safety challenges. This project brings forward an innovative idea – can increasing the agility of an autonomous vehicle improve its safety? This notion is somewhat controversial since agility (like that of race cars) is more frequently associated with decreased safety margins.Motivated by these challenges underlying real-world testing and safety for autonomous vehicles, the goal of this project is to develop the foundations for autonomous cyber-physical systems along two dimensions: agility, safety, and their interplay. The project is centered on (1) increasing agility for AVs by developing new methods for agile motion planning, so they can maneuver at the limits of their handling and control when it matters most to escape potentially unsafe conditions, (2) automated reasoning about uncertain dynamic situations that may occur during autonomous CPS operation, and (3) developing novel methods for automatically generating testing and edge-case scenarios at design time, to explore scenarios under which the autonomous CPS would fail. The proposed methods will be evaluated on scaled autonomous vehicles testbeds, on photorealistic and high-fidelity simulation platforms, and on full scale AV prototypes. The project will also consider not just safety of an unoccupied AV – but one in which passengers may be present. This CAREER project includes designing exciting new courses, and initiatives centered around autonomous racing to engage with research and mentoring for K-12, undergraduate, and graduate students. The project aims to ensure that students cultivate a holistic view of cyber-physical systems and autonomous systems by drawing stronger connections between theory, applications, and hands-on platform development. The project will help enhance the capabilities of autonomous cyber-physical systems and facilitate with their safe deployment.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.

自主网络物理系统（CPS），如自动驾驶汽车和无人机，由深度学习和基于AI的感知，规划和控制算法提供动力，正在形成我们国家重要基础设施的基础，并提出直接，紧迫的安全关键挑战。目前部署自主CPS的方法的一个主要限制是确保系统在正常运行条件下不经常发生的情况下安全可靠地运行，因此难以收集数据。例如，经过训练遵守“道路规则”的自动驾驶汽车在大多数情况下都会表现良好，但这是不寻常的条件，边缘情况，这是最大的安全挑战。 该项目提出了一个创新的想法-增加自动驾驶汽车的敏捷性是否可以提高其安全性？ 这个概念有些争议，因为敏捷性（如赛车汽车）更经常与安全裕度降低相关。受这些挑战的激励，自动驾驶汽车的真实世界测试和安全性，这个项目的目标是沿着沿着两个维度开发自主网络物理系统的基础：敏捷性，安全性和它们的相互作用。 该项目的重点是（1）通过开发敏捷运动规划的新方法来提高自动驾驶汽车的敏捷性，因此当最重要的是逃离潜在的不安全条件时，它们可以在操纵和控制的极限下进行机动，（2）自动推理自主CPS操作期间可能发生的不确定动态情况，以及（3）开发用于在设计时自动生成测试和边缘情况场景的新颖方法，以探索自主CPS将失败的场景。所提出的方法将在缩放的自动驾驶汽车测试平台、逼真和高保真的仿真平台以及全尺寸AV原型上进行评估。 该项目还将不仅考虑无人驾驶汽车的安全性，而且考虑乘客可能在场的安全性。 这个职业生涯项目包括设计令人兴奋的新课程，以及围绕自主赛车的倡议，以参与K-12，本科生和研究生的研究和指导。该项目旨在确保学生通过在理论，应用和动手平台开发之间建立更强的联系，培养对网络物理系统和自主系统的整体看法。该项目将有助于增强自主网络物理系统的能力，并促进其安全部署。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

Adaptive Lookahead Pure-Pursuit for Autonomous Racing

自适应前瞻 Pure-Pursuit 自动驾驶赛车

• 发表时间: 2021
• 期刊: ArXivorg
• 作者: Sukhil, Varundev;Behl, Madhur
• 通讯作者: Behl, Madhur

Threading the Needle—Overtaking Framework for Multi-agent Autonomous Racing

穿针引线——多智能体自主赛车超车框架

• DOI: 10.4271/12-05-01-0004
• 发表时间: 2022
• 期刊: SAE International Journal of Connected and Automated Vehicles
• 作者: Suresh Babu, Varundev;Behl, Madhur
• 通讯作者: Behl, Madhur

Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

• DOI: 10.1109/ojits.2022.3181510
• 发表时间: 2022-02
• 期刊: IEEE Open Journal of Intelligent Transportation Systems
• 影响因子: 2.6
• 作者: Johannes Betz;Hongrui Zheng;Alexander Liniger;Ugo Rosolia;Phillip Karle;Madhur Behl;V. Krovi;Rahul Mangharam

Scenario2Vector: scenario description language based embeddings for traffic situations

• DOI: 10.1145/3450267.3450544
• 发表时间: 2021-05
• 期刊: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems
• 作者: Aron Harder;Jaspreet Ranjit;Madhur Behl

Towards multi-agent autonomous racing with the Deepracing framework.

使用 Deepracing 框架实现多智能体自主赛车。

• 发表时间: 2021
• 期刊: International Conference on Robotics and Automation (ICRA
• 作者: Weiss, Trent;Chrosniak, John;Behl, Madhur
• 通讯作者: Behl, Madhur