CPS: Synergy: Verified Control of Cooperative Autonomous Vehicles

该项目研究在各种环境条件下为机动自动驾驶车辆（“自动驾驶汽车”）构建安全控制算法的技术。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在高速公路和地面街道上行驶。然而，未来的车辆将需要处理坑洞，雪，大风，驾驶雨，飞镖动物，雾和所有其他障碍，使驾驶在真实的世界中的挑战摆在首位。这些条件中的一些需要以突然加速、制动和/或快速转向的形式的“激进机动”。这种激进的机动对现有的自主算法提出了重大挑战，引起了对乘客、道路上其他车辆和行人安全的担忧。同时，在自主操作时保证安全行为对于采用这些系统至关重要，而这种保证要求开发可靠和经过验证的操纵。位于博尔德的科罗拉多大学的Ninja Car平台是经过验证的算法的实验平台，也用于教育学生和爱好者设计和实现自动驾驶汽车。在该项目中进行的研究有助于实现安全的自动驾驶汽车的最终愿景，重点是机动的安全算法。此外，为了培养新一代的学生和普通民众的自动驾驶汽车的设计和运用，本项目开发了能够适应并保证各种行驶条件的可靠的自动驾驶地面车辆的操纵控制系统。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用于机动调节的正式验证的控制算法以及可以针对不同道路条件执行的相应机动集合。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根据汽车的实际动态（例如冲击磨损，轮胎压力等）进行微调控制。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，合作车辆实施验证的防撞方案，并共享驾驶条件（例如，如何光滑的一个给定的道路实际上是），制定环境感知，驾驶安全的maneuvers.The研究扩展了越来越多的工作，应用形式化的方法，严格解决控制问题。横向控制李雅普诺夫和障碍函数的框架提供了解决非线性动力系统的轨迹跟踪问题的基础。这项工作还研究了新的约束求解方法，为非线性系统合成这些功能。这项研究是使用一个1/8比例的模型测试平台，称为忍者车在科罗拉多大学，博尔德。研究理念也被整合到教育活动中，使用Ninja Car作为一个具有成本效益的系统，指导各级工程专业的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。

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.