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

该项目旨在改变现代汽车的软件开发过程，随着许多新的自动驾驶功能的引入，现代汽车正在经历重大创新，最终实现全自动驾驶。这些新功能中的大多数确实是在软件中实现的，其核心是几个控制算法。这种控制算法在一个反馈回路中运行，包括感知工厂或被控制系统的状态，计算控制输入，并驱动工厂以强制其执行期望的行为。这方面的例子包括刹车和发动机控制、巡航控制、自动泊车和全自动驾驶。目前的开发流程是从数学上设计一个控制器开始，然后在汽车中现有的嵌入式系统的软件中实现它。这种流程在过去很有效，因为汽车嵌入式系统很简单——只有很少的处理器、通信总线和简单的传感器。控制算法简单，重要功能主要由机械子系统实现。但现代汽车有超过100个处理器，由几英里长的电缆连接起来，还有多个传感器，如摄像头、雷达和激光雷达，这些传感器的数据需要经过复杂的处理才能被控制器使用。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的自治功能。因此，在这样一个复杂的硬件/软件系统中，计算、通信和内存访问现在可以用许多不同的方式进行组织，每一种方式都与精度、时间和资源需求方面的不同权衡相关联。这些反过来又对控制性能和如何设计控制策略有相当大的影响。因此，设计控制器和在汽车软件中实现控制器之间的明确分离不再有效。该项目旨在发展理论基础和工具支持，以适应新兴的汽车控制策略和嵌入式系统的设计流程。这不仅将使未来的汽车设计更具成本效益，而且还将有助于对所实施的控制器进行认证，从而实现更安全的自动驾驶汽车。特别是，目标是在分布式嵌入式架构上自动合成和实现控制算法，该架构由不同类型的多核处理器、gpu、基于fpga的加速器、不同的通信总线、网关和与计算密集型处理相关的传感器组成。从设备规格、控制目标、控制器模板和部分指定的实现架构开始，该项目寻求综合控制器和实现架构参数，以满足所有控制目标和资源约束。为此，将使用从切换控制，接口兼容性检查和多模式系统调度的各种技术，这些技术将控制理论，实时系统，程序分析和数学优化结合在一起。与通用汽车公司合作，该项目将建立一个工具链，将控制器设计工具（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汽车公司的一系列挑战问题，展示这种新设计流程和工具支持的好处。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。

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.