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CPS: Synergy: Verified Control of Cooperative Autonomous Vehicles

CPS：协同：协作自动驾驶车辆的验证控制

基本信息

批准号：
1646556
负责人：
Christoffer Heckman
金额：
$ 77.67万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-10-01 至 2020-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646556&HistoricalAwards=false
关键词：
CPS Synergy Verified Control Cooperative

项目摘要

The project studies techniques for constructing guaranteed-safe control algorithms for maneuvering autonomous vehicles ("self-driving cars") under a variety of environmental conditions. Existing autonomous vehicles are able to navigate highways and surface streets reliably when the driving conditions do not pose significant challenges. However, future vehicles will need to handle pot-holes, snow, high winds, driving rain, darting animals, fog and all the other impediments that make driving in the real world challenging in the first place. Some of these conditions require "aggressive maneuvers" in the form of sudden acceleration, braking and/or rapid steering. Such aggressive maneuvers present significant challenges to existing autonomy algorithms, raising concerns regarding the safety of the passengers, other vehicles on the road and pedestrians. At the same time, guaranteeing safe behavior while in autonomous operation is critical for the adoption of these systems, and such guarantees demand the development of reliable and verified maneuvering. The Ninja Car platform at the University of Colorado, Boulder serves as an experimental platform for the verified algorithms, and is also used to educate students and enthusiasts on the design and implementation of autonomous vehicles. The research carried out in this project contributes to the ultimate vision of self-driving cars that are safe by focusing on guaranteed-safe algorithms for maneuvering. Furthermore, the educational activities seek to educate a new generation of students and enthusiasts from the general public on the design and deployment of self-driving cars.This project develops reliable control systems for maneuver regulation in autonomous ground vehicles that are adaptive to, and guaranteed for, a variety of driving conditions. The approach first considers the problem of developing a stack of increasingly complex models for autonomous vehicles. The simplest models serve to develop formally verified control algorithms for maneuver regulation and the corresponding set of maneuvers that can be carried out for varying road conditions. These results are transferred to more sophisticated models that use on-board sensors to fine-tune the control to the actual dynamics of the car (such as the wear on the shocks, tire pressure, etc.). Finally, building upon verified maneuvers for a single vehicle, the project studies cooperative maneuvers for multiple vehicles, wherein the vehicles communicate to meaningfully share information. The cooperating vehicles then implement verified collision avoidance schemes and share driving conditions (e.g. how slick a given road actually is) to formulate environment-aware, guaranteed-safe maneuvers.The research extends the growing body of work on applying formal methods for rigorously solving control problems. A framework of transverse control Lyapunov and barrier functions provides a basis for solving trajectory tracking problems for nonlinear dynamical systems. The work also investigates new constraint-solving approaches for synthesizing these functions for nonlinear systems. The research is evaluated using a 1/8th-scale model testbed called the Ninja Car at the University of Colorado, Boulder. The research ideas are also integrated into educational activities that use the Ninja Car as a cost effective system for instructing engineering students at all levels, and enthusiasts interested in autonomous vehicles, on the fundamental principles that underlie the design and deployment of these systems.

该项目研究在各种环境条件下为机动自动驾驶汽车(“自动驾驶汽车”)构建保证安全的控制算法的技术。现有的自动驾驶汽车能够在驾驶条件不构成重大挑战的情况下可靠地导航高速公路和地面街道。然而，未来的汽车将需要处理坑洞、雪、大风、暴雨、飞奔的动物、雾和所有其他障碍，这些障碍首先会让现实世界中的驾驶变得具有挑战性。其中一些情况需要以突然加速、刹车和/或快速转向的形式进行“攻击性机动”。这种激进的操作对现有的自动驾驶算法构成了重大挑战，引发了对乘客、道路上其他车辆和行人安全的担忧。同时，确保自主操作时的安全行为是采用这些系统的关键，这种保证要求开发可靠和经过验证的机动。科罗拉多大学博尔德分校的忍者汽车平台是经过验证的算法的实验平台，也被用来教育学生和爱好者如何设计和实现自动驾驶汽车。该项目中开展的研究通过专注于保证安全的机动算法，为自动驾驶汽车安全的终极愿景做出了贡献。此外，教育活动旨在教育新一代学生和普通公众爱好者如何设计和部署自动驾驶汽车。该项目开发了可靠的控制系统，用于自动地面车辆的机动调节，适应并保证各种驾驶条件。该方法首先考虑为自动驾驶汽车开发一系列日益复杂的模型的问题。最简单的模型用于开发经过正式验证的机动调节控制算法，以及可针对不同路况执行的相应机动集。这些结果被转移到更复杂的模型中，这些模型使用车载传感器来微调控制，以适应汽车的实际动态(如冲击磨损、轮胎压力等)。最后，在单个车辆的验证机动的基础上，该项目研究了多个车辆的协同机动，其中车辆进行通信以有意义地共享信息。然后，合作车辆实施经过验证的防撞方案，并分享驾驶条件(例如，给定的道路实际有多光滑)，以制定环境感知、保证安全的操作。这项研究扩展了越来越多的工作，即应用形式方法严格解决控制问题。横向控制Lyapunov函数和障碍函数的框架为解决非线性动力系统的轨迹跟踪问题提供了基础。这项工作还探索了新的约束求解方法，用于综合非线性系统的这些函数。这项研究是使用科罗拉多大学博尔德分校名为忍者汽车的1/8比例模型试验台进行评估的。研究思路还被整合到教育活动中，使用忍者汽车作为一个具有成本效益的系统，指导各级工程专业学生和对自动驾驶汽车感兴趣的爱好者，了解这些系统设计和部署的基本原则。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning Lyapunov (Potential) Functions from Counterexamples and Demonstrations

从反例和演示中学习李亚普诺夫（势）函数

DOI：
10.15607/rss.2017.xiii.049
发表时间：
2017
期刊：
Robotics: Science and Systems
影响因子：
0
作者：
Ravanbakhsh, Hadi;Sankaranarayanan, Sriram
通讯作者：
Sankaranarayanan, Sriram

Online System Identification and Calibration of Dynamic Models for Autonomous Ground Vehicles

自主地面车辆动态模型的在线系统识别和校准

DOI：
10.1109/icra.2018.8460691
发表时间：
2018
期刊：
IEEE International Conference on Robotics and Automation (ICRA
影响因子：
0
作者：
Aghli, Sina;Heckman, Christoffer
通讯作者：
Heckman, Christoffer

Model Predictive Real-Time Monitoring of Linear Systems

DOI：
10.1109/rtss.2017.00035
发表时间：
2017-12
期刊：
2017 IEEE Real-Time Systems Symposium (RTSS)
影响因子：
0
作者：
Xin Chen;S. Sankaranarayanan
通讯作者：
Xin Chen;S. Sankaranarayanan

Distributed Online Convex Programming for Collision Avoidance in Multi-agent Autonomous Vehicle Systems

多智能体自主车辆系统中避免碰撞的分布式在线凸规划

DOI：
10.23919/acc.2019.8814857
发表时间：
2019
期刊：
American Control Conference (ACC
影响因子：
0
作者：
Ding, Guohui;Ravanbakhsh, Hadi;Liu, Zhiyuan;Sankaranarayanan, Sriram;Chen, Lijun
通讯作者：
Chen, Lijun

Trajectory Tracking Control for Robotic Vehicles using Counterexample Guided Training of Neural Networks

使用神经网络反例引导训练的机器人车辆轨迹跟踪控制