CPS: Medium: GOALI: Design Automation for Automotive Cyber-Physical Systems

CPS：中：GOALI：汽车网络物理系统设计自动化

• 批准号: 2038960
• 负责人: Samarjit Chakraborty
• 金额: $ 120万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038960&HistoricalAwards=false
• 关键词: CPS; Medium; GOALI; Design; Automation; CPS; Medium; GOALI; Design; Automation

This project aims to transform the software development process in modern cars, which are witnessing significant innovation with many new autonomous functions being introduced, culminating in a fully autonomous vehicle. Most of these new features are indeed implemented in software, at the heart of which lies several control algorithms. Such control algorithms operate in a feedback loop, involving sensing the state of the plant or the system to be controlled, computing a control input, and actuating the plant in order to enforce a desired behavior on it. Examples of this range from brake and engine control, to cruise control, automated parking, and to fully autonomous driving. Current development flows start with mathematically designing a controller, followed by implementing it in software on the embedded systems existing in a car. This flow has worked well in the past, where automotive embedded systems were simple – with few processors, communication buses, and simple sensors. The control algorithms were simple as well, and important functions were largely implemented by mechanical subsystems. But modern cars have over 100 processors connected by several miles of cables, and multiple sensors like cameras, radars and lidars, whose data needs complex processing before it can be used by a controller. Further, the control algorithms themselves are also more complex since they need to implement new autonomous features that did not exist before. As a result, both computation, communication, and memory accesses in such a complex hardware/software system can now be organized in many different ways, with each being associated with different tradeoffs in accuracy, timing, and resource requirements. These in turn have considerable impact on control performance and how the control strategy needs to be designed. As a result, the clear separation between designing the controller, followed by implementing it in software in the car, no longer works well. This project aims to develop both the theoretical foundations and the tool support to adapt this design flow to emerging automotive control strategies and embedded systems. This will not only result in more cost-effective design of future cars, but will also help with certifying the implemented controllers, thereby leading to safer autonomous cars. In particular, the goal is to automate the synthesis and implementation of control algorithms on distributed embedded architectures consisting of different types of multicore processors, GPUs, FPGA-based accelerators, different communication buses, gateways, and sensors associated with compute-intensive processing. Starting with specifications of plants, control objectives, controller templates, and a partially-specified implementation architecture, this project seeks to synthesize both controller and implementation architecture parameters that meet all control objectives and resource constraints. Towards this, a variety of techniques from switched control, interface compatibility checking, and scheduling of multi-mode systems – that bring together control theory, real-time systems, program analysis, and mathematical optimization, will be used. In collaboration with General Motors, this project will build a tool chain that integrates controller design tools like Matlab/Simulink with standard embedded systems design and configuration tools. This project will demonstrate the benefits of this new design flow and tool support by addressing a set of challenge problems from General Motors.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.

该项目旨在改变现代汽车的软件开发过程，这些工具正在见证了重大创新，并引入了许多新的自主功能，并在完全自动驾驶汽车中最终导致。这些新功能中的大多数确实是在软件中实现的，这是多种控制算法的核心。这种控制算法在反馈循环中运行，涉及要控制的植物状态或系统的状态，计算控制输入并驱动植物以在其上执行所需的行为。此范围从制动和发动机控制到巡航控制，自动停车以及完全自动驾驶的示例。当前的开发流程从数学上设计控制器开始，然后在汽车中存在的嵌入式系统上实现它。过去，这种流程运行良好，那里的汽车嵌入式系统很简单 - 很少有处理器，通信总线和简单的传感器。对照算法也很简单，重要的功能在很大程度上由机械子系统实现。但是，现代汽车的100多个处理器通过几英里的电缆连接，以及摄像机，雷达和激光镜等多个传感器，它们的数据需要复杂的处理，然后才能由控制器使用。此外，控制算法本身也更加复杂，因为它们需要实现以前不存在的新自主功能。结果，现在可以以许多不同的方式组织了如此复杂的硬件/软件系统中的计算，通信和内存访问，每种都与准确性，时机和资源需求的不同权衡相关联。这些反过来又考虑了对控制绩效以及如何设计控制策略的影响。结果，设计控制器，然后在汽车中的软件中实现它之间的明确分离，不再奏效。该项目旨在开发理论基础和工具支持，以使这种设计流适应新兴的汽车控制策略和嵌入式系统。这不仅会导致更具成本效益的未来汽车设计，而且还将有助于认证实施控制器，从而导致更安全的自动驾驶汽车。特别是，目标是在分布式嵌入式体系结构上自动化和实现控制算法的综合和实现，该架构由不同类型的多层处理器，GPU，GPU，基于FPGA的加速器，不同的通信总线，网关，网关和与计算密集型处理相关的传感器。从植物，控制对象，控制器模板和部分指定的实现体系结构的规格开始，该项目旨在综合符合所有控制对象和资源约束的控制器和实现架构参数。为此，将使用转换控制，接口兼容性检查以及多模式系统的各种技术 - 将使用控制理论，实时系统，程序分析和数学优化的多种模式系统。该项目将与通用电动机合作，将建立一个工具链，该工具链将控制器设计工具（例如MATLAB/SIMULINK）与标准嵌入式系统设计和配置工具集成在一起。该项目将通过解决通用电机的一系列挑战问题来证明这种新设计流和工具支持的好处。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，通过评估诚实地认为支持。