Controller design and their software implementations are usually done in isolated design spaces using respective COTS design tools. However, this separation of concerns can lead to long debugging and integration phases. This is because assumptions made about the implementation platform during the design phase—e.g., related to timing—might not hold in practice, thereby leading to unacceptable control performance. In order to address this, several control/architecture co-design techniques have been proposed in the literature. However, their adoption in practice has been hampered by the lack of design flows using commercial tools. To the best of our knowledge, this is the first article that implements such a co-design method using commercially available design tools in an automotive setting, with the aim of minimally disrupting existing design flows practiced in the industry. The goal of such co-design is to jointly determine controller and platform parameters in order to avoid any design-implementation gap , thereby minimizing implementation time testing and debugging. Our setting involves distributed implementations of control algorithms on automotive electronic control units ( ECUs ) communicating via a FlexRay bus. The co-design and the associated toolchain Co-Flex jointly determines controller and FlexRay parameters (that impact signal delays) in order to optimize specified design metrics. Co-Flex seamlessly integrates the modeling and analysis of control systems in MATLAB/Simulink with platform modeling and configuration in SIMTOOLS/SIMTARGET that is used for configuring FlexRay bus parameters. It automates the generation of multiple Pareto-optimal design options with respect to the quality of control and the resource usage, that an engineer can choose from. In this article, we outline a step-by-step software development process based on Co-Flex tools for distributed control applications. While our exposition is automotive specific, this design flow can easily be extended to other domains.

Low-power wireless communication is a central building block of Cyber-physical Systems and the Internet of Things. Conventional low-power wireless protocols make avoiding packet collisions a cornerstone design choice. The concept of synchronous transmissions challenges this view. As collisions are not necessarily destructive, under specific circumstances, commodity low-power wireless radios are often able to receive useful information even in the presence of superimposed signals from different transmitters. We survey the growing number of protocols that exploit synchronous transmissions for higher robustness and efficiency as well as unprecedented functionality and versatility compared to conventional designs. The illustration of protocols based on synchronous transmissions is cast in a conceptional framework we establish, with the goal of highlighting differences and similarities among the proposed solutions. We conclude the paper with a discussion on open research questions in this field.

Wired fieldbuses have long been proven effective in supporting Cyber-Physical Systems (CPS). However, various domains are now striving for wireless solutions due to ease of deployment or novel functionality requiring the ability to support mobile devices. Low-power wireless protocols have been proposed in response to this need, but requirements of a large class of CPS applications can still not be satisfied. We thus propose Time-Triggered Wireless (TTW), a distributed low-power wireless system design that minimizes communication energy consumption and offers end-to-end timing predictability, runtime adaptability, reliability, and low latency. Evaluation shows a 2× reduction in communication latency and 33-40% lower radio-on time compared with DRP, the closest related work, validating the suitability of TTW for new exciting wireless CPS applications.

Software-based control of physical systems is common in domains such as automotive, avionics, and industrial automation. Safety of such systems is determined by control-theoretic properties such as stability, settling time, and peak overshoot. These properties strongly depend on the software code generated from high-level controller models, and the implementation of such code on an embedded platform. To ensure safety, the semantics of the system model considered for controller design must be faithfully preserved in the platform implementation. However, traditionally, controller design and implementation platform design are carried out in isolation, followed by their integration, which often relies on simulations to estimate the behavior of the controllers. Thus, safety properties that were proven at the model level using control-theoretic tools can no longer be established in an actual implementation. This makes the design of embedded control systems costly, error prone, and hinders certification. In this paper, we review recent efforts in control-platform cosynthesis techniques toward addressing this problem. Here, the control and the embedded systems communities have come together to adopt a cyber-physical system (CPS)-oriented design paradigm. This cosynthesis paradigm integrates the design of control algorithms and platform parameters within a holistic optimization framework and accounts for relevant details from both sides. We survey the evolution of design approaches for such cosynthesis and show how-the originally disjoint-controller and the platform design methods are gradually converging.

Current automotive E/E architectures are comprised of hardware and software and are mostly designed in a monolithic approach, static over the lifetime of the vehicle. Design, implementation and updates are mostly performed on a per-component-basis, exchanging complete Electronic Control Units (ECUs) or their software image as a whole. With an increasing amount of functionality being realized in software, the benefits of software can be used increasingly. This includes modularization of components, which forms the basis for updates and addition of functions. Additionally, this modularization allows the consolidation of ECUs and supports a higher level of integration. Such modularization and dynamic behavior over the lifetime of a vehicle feet, as well as a single vehicle does, however, hold a lot of challenges for safety-critical systems. Safety-critical systems, such as cars, require their behavior to be deterministic. The design of such modular systems needs to consider and cope with uncertainties in modular architectures. This paper highlights some of the dimensions of uncertainty, which will exist in future E/E architectures and presents initial approaches on how to manage these.

Modern automotive systems consist of hundreds of functionalities implemented in software. Moreover, these functionalities are constantly evolving with increasing demand for automation, industry competition and changing sensor and actuator capabilities. Correspondingly, it is important to adapt the engineering and software development processes for such systems to consider fast management of this evolution at minimum cost. Towards this, in this paper, we outline three different problems in the context of evolving automotive software and discuss potential solutions for each of them. First, we outline a framework that can accommodate variability in specifications while developing software for automotive product lines. Secondly, a technique is illustrated to addresses after-sales addition of new features in existing systems by studying corresponding acceptable performance degradation of existing functionalities. Finally, we discuss how an inconsistency management framework and regression verification can ensure consistent evolution of engineering processes for automotive mechatronic systems.

Increasingly more software-based applications are being developed and deployed in modern vehicles. As a result, the extensibility of a system design has become an important issue in order to accommodate more future applications and update of existing ones on one hand and reduce the effort and cost of re-design, test and validation on the other. In this paper, we discuss the extensibility-driven design in the automotive E/E architecture. We explain the motivation for such a design objective and discuss the definition of extensibility metric and extensibility-driven design methods under two different setting, namely the system based on CAN bus and FlexRay bus. Based on these two examples, we illustrate the importance and advantages of extensibility-driven design in the automotive E/E architecture.

Recent advances in battery and drone technologies have opened up possibilities of commercial use of drones. Private companies are looking into the possibilities of using drones for commercial deliveries from the legal, technical, and economical perspective. Nevertheless, the battery management perspective of such businesses has not yet been thoroughly investigated. In this paper, we identify that battery management of such application has a major impact of the costs, and formulate an optimization problem to reduce the aging of batteries. We identify two sub-problems, battery assignment, and battery scheduling to derive a solution that minimizes the aging of the batteries. We show that the formulation enables leveraging the trade-off relationships between the packet waiting time and battery purchasing cost. The experimental results show the proposed method reduce the electricity and battery purchasing cost by 25%, and average packet waiting time by more than 50%.

The rapid growth in the size and complexity of modern Cyber-Physical Systems (CPS) imposes increasing demand for the embedded resources, especially the communication resources. As a result, resource-efficient CPS design has become an important issue. Towards the design of networked embedded control systems, a major branch of CPS, reliable and deterministic communication is able to achieve satisfactory control performance. However, the amount of this type of resource that can be provided by the embedded platform is often limited. On the other hand, it is difficult to guarantee the control performance with non-deterministic communication resources, due to their unpredictable behavior. In this paper, we propose a novel control scheme to efficiently utilize elastic communication resources. In general, the non-deterministic communication resources are flexibly deployed on top of the deterministic communication resources to achieve stability and good control performance. In the rare worst-case, when non-deterministic communication is completely unavailable, the deterministic communication resources are used to guarantee stability and the control performance satisfying the design requirement. The experimental results show that the performance of the control application is ensured to satisfy the design requirement in the worst case and that better control performance is achieved when non-deterministic resources are available.

One emerging research direction to address the design of Cyber-Physical Systems (CPS) is the co-design of the architecture and the controllers. The co-design techniques integrate the design of control and architecture in an early phase and the parameters on both sides can be synthesized according to certain design objectives. This explores the characteristics on both sides to achieve more efficient design of such systems. In this paper, we give an overview of the automated synthesis of CPS from joint controller/architecture specifications by explaining the background and motivation for such methods and illustrating this design paradigm with a concrete example of a FlexRay-based embedded control system. Furthermore, we provide the future outlook in this direction by explaining possible extensions and the related challenges.

Automotive platforms today run hundreds of millions of lines of software code implementing a large number of different control applications spanning across safety-critical functionality to driver assistance and comfort-related functions. While such control software today is largely designed following model-based approaches, the underlying models do not take into account the details of the implementation platforms, on which the software would eventually run. Following the state-of-the-art in control theory, the focus in such design is restricted to ensuring the stability of the designed controllers and meeting control performance objectives, such as settling time or peak overshoot. However, automotive platforms are highly cost-sensitive and the issue of designing “resource-efficient” controllers has largely been ignored so far and is addressed using very ad hoc techniques. In this paper, we will illustrate how, following traditional embedded systems design oriented thinking, computation, communication and memory issues can be incorporated in the controller design stage, thereby resulting in control software not only satisfying the usual control performance metrics but also making efficient utilization of the resources on distributed automotive architectures.

Drones, also referred to as unmanned aerial vehicles (UAVs), are recently expanding their field of usage beyond military surveillance and tactical applications. Commercial drone delivery service is one of the promising applications in the near future, and a number of companies are already pushing forward the legal and technical barriers to realize the concept. Unlike conventional applications of drones, the success of a commercial application depends critically on the profitability. The major sources of expense are the electricity cost and battery purchasing cost due to aging. Hence, it is crucial to maximize the energy efficiency and mitigate battery aging of the drone delivery business. However, no prior work has extensively assessed the problem for the business as a whole. This paper, for the first time, proposes a holistic and detailed analysis on the profitability and time to delivery of the drone delivery business. This paper identifies the major design parameters and runtime management potentials that affect the profitability and time to delivery of the business. We have implemented a discrete event simulator based on detailed models of the comprising components. We perform a design space exploration to understand the effects of the various battery configurations, battery attachment technique, drone flight speed, etc., on time to delivery, electricity cost, and battery purchasing cost. Our results show that such control knobs have a significant impact on the time to delivery and the operating cost of the business.

The development of automotive electronics and software systems is often associated with high costs due to their multi-domain nature (including control engineering, electronics, hydraulics, mechanics, etc). The involvement of these different disciplines makes it difficult for control engineers to test their controllers with them being integrated in the whole system, in early development phases. By introducing the "XiL approach", ETAS wants to leverage virtualization techniques in order to bring embedded systems to the desk of every developer. The XiL approach implies fast development cycles due to easy integration of software, vehicle and plant components on the PC. XiL strives for seamless transition between X-in-the-loop setups, with X representing any control model (M), software (S), or hardware (H) under test. This paper will outline this methodology with an engine management system example.

Recently, research on control and architecture co- design has been drawing increasingly more attention. This is because these techniques integrate the design of the controllers and the architecture and explore the characteristics on both sides to achieve more efficient design of embedded control systems. However, there still exist several challenges like the large design space and inadequate trade-off opportunities for different objectives like control performance and resource utilization. In this paper, we propose a co-optimization approach for FlexRay-based distributed control systems, that synthesizes both the controllers and the task and communication schedules. This approach exploits some FlexRay protocol specific characteristics to reduce the complexity of the whole optimization problem. This is done by employing a customized control design and a nested two-layered optimization technique. Therefore, compared to existing methods, the proposed approach is more scalable. It also allows multi-objective optimization taking into account both the overall control performance and the bus resource utilization. This approach generates a Pareto front representing the trade-offs between these two, which allows the engineers to make suitable design choices.

The innovation in the automotive domain is shifting considerably to the Electrical/Electronics system and software. The evolution cycle of the electronic system and the software is significantly shorter than the life cycle of a vehicle and therefore the functionality of a vehicle might become 'outdated' easily in the future. Thus, it would be advantageous if new applications can be installed or existing applications can be upgraded via cloud services after sales in a plug-and-play fashion. This requires that the underlying system possesses a certain degree of adaptivity and reconfigurability. One important issue in this case is the allocation of computation and communication resources. In the case of a time-triggered system, the task and network schedules need to be adapted to accommodate new applications. Towards addressing this problem, we propose a schedule management framework to obtain, synthesize and manage schedules efficiently online for Ethernet-based time-triggered systems in the automotive context. This framework is based on a client-server architecture and each side consists of a web module, a synthesis module and a configuration pool. It utilizes the Internet access of modern vehicles to exploit the computation and storage capacity on the server in a cloud-computing manner and can facilitate the reuse of generated schedule sets. In the synthesis module, a four-stage strategy is introduced to reduce the synthesis time and the disturbance to existing applications. The experimental results show that the proposed framework can be applied to generate and manage schedules online and benefit from both onboard and cloud-based schedule synthesis. The result also shows the applicability of the introduced four-stage synthesis~strategy.

In this paper we consider the case of a network of Electronic Control Units (ECUs) connected through a Flex Ray bus in the automotive domain. Multiple distributed applications can run on this underlying architecture, each partitioned into tasks that are mapped on different ECUs. These applications can often be executed in different functional modes with different requirements on the communication resources in terms of data size and sampling period. Moreover, new applications can be deployed on to the ECUs at run-time. To efficiently utilize the communication resources and accommodate new applications, a certain flexibility in reallocation of the resource is necessary. However, the Flex Ray bus requires static configuration of schedules and data mapping in order to guarantee a more deterministic system behavior, allowing little room for flexibility. In order to address this problem, we propose a reconfigurable communication middleware that lies between the application layer and the communication controller layer, which maps messages onto Flex Ray schedules, and can be reconfigured at runtime. The configuration is synthesized and deployed online, allowing a certain reallocation of communication resources to applications. In this paper, we describe the design of such a reconfigurable communication middleware and demonstrate its function with an implementation using industry-strength Flex Ray design tools.

In this paper, we study time-triggered distributed systems where periodic application tasks are mapped onto different end stations (processing units) communicating over a switched Ethernet network. We address the problem of application level (i.e., both task- and network-level) schedule synthesis and optimization. In this context, most of the recent works [10], [11] either focus on communication schedule or consider a simplified task model. In this work, we formulate the co-synthesis problem of task and communication schedules as a Mixed Integer Programming (MIP) model taking into account a number of Ethernet-specific timing parameters such as interframe gap, precision and synchronization error. Our formulation is able to handle one or multiple timing objectives such as application response time, end-to-end delay and their combinations. We show the applicability of our formulation considering an industrial size case study using a number of different sets of objectives. Further, we show that our formulation scales to systems with reasonably large size.

In this paper we study distributed automotive control applications whose tasks are mapped onto different ECUs communicating via a switched Ethernet network. As traditional automotive communication buses like CAN, FlexRay, LIN and MOST are gradually reaching their performance limits because of the increasing complexity of automotive architectures and applications, Ethernet-based in-vehicle communication systems have attracted a lot of attention in recent times. However, currently there is very little work on systematic timing analysis for Ethernet which is important for its deployment in safety-critical scenarios like in an automotive architecture. In this work, we propose a compositional timing analysis technique that takes various features of switched Ethernet into account like network topology, frame priorities, communication delay, memory requirement on switches, performance, etc. Such an analysis technique is particularly suitable during early design phases of automotive architectures and control software deployment. We demonstrate its use in analyzing mixed-criticality traffic patterns consisting of messages from performance-oriented control loops and timing-sensitive real-time tasks. We further evaluate the tightness of the obtained analytical bounds with an OMNeT++ based network simulation environment, which involves long simulation time and does not provide formal guarantees.

In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example caused by occlusions. By fusing the depth images of all available cameras into one joint point cloud, we can seamlessly incorporate the available information from multiple sensors into the pose estimation. To track the high-dimensional human pose, we employ state-of-the-art annealed particle filtering and partition sampling. We compute the particle likelihood based on the truncated signed distance of each observed point to a parameterized human shape model. We apply a coarse-to-fine scheme to recognize a wide range of poses to initialize the tracker. In our experiments, we demonstrate that our approach can accurately track human motion in real-time (15Hz) on a GPGPU. In direct comparison to two existing trackers (OpenNI, Microsoft Kinect SDK), we found that our approach is significantly more robust for unconstrained motions and under (partial) occlusions.