Robust and secure resource management for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.Modern vehicles are examples of complex cyber-physical systems with tens to hundreds of interconnected Electronic Control Units (ECUs) that manage various vehicular subsystems. With the shift towards autonomous driving, emerging vehicles are being characterized by an increase in the number of hardware ECUs, greater complexity of applications (software), and more sophisticated in-vehicle networks. These advances have resulted in numerous challenges that impact the reliability, security, and real-time performance of these emerging automotive systems. Some of the challenges include coping with computation and communication uncertainties (e.g., jitter), developing robust control software, detecting cyber-attacks, ensuring data integrity, and enabling confidentiality during communication. However, solutions to overcome these challenges incur additional overhead, which can catastrophically delay the execution of real-time automotive tasks and message transfers. Hence, there is a need for a holistic approach to a system-level solution for resource management in automotive cyber-physical systems that enables robust and secure automotive system design while satisfying a diverse set of system-wide constraints. ECUs in vehicles today run a variety of automotive applications ranging from simple vehicle window control to highly complex Advanced Driver Assistance System (ADAS) applications. The aggressive attempts of automakers to make vehicles fully autonomous have increased the complexity and data rate requirements of applications and further led to the adoption of advanced artificial intelligence (AI) based techniques for improved perception and control. Additionally, modern vehicles are becoming increasingly connected with various external systems to realize more robust vehicle autonomy. These paradigm shifts have resulted in significant overheads in resource constrained ECUs and increased the complexity of the overall automotive system (including heterogeneous ECUs, network architectures, communication protocols, and applications), which has severe performance and safety implications on modern vehicles. The increased complexity of automotive systems introduces several computation and communication uncertainties in automotive subsystems that can cause delays in applications and messages, resulting in missed real-time deadlines. Missing deadlines for safety-critical automotive applications can be catastrophic, and this problem will be further aggravated in the case of future autonomous vehicles. Additionally, due to the harsh operating conditions (such as high temperatures, vibrations, and electromagnetic interference (EMI)) of automotive embedded systems, there is a significant risk to the integrity of the data that is exchanged between ECUs which can lead to faulty vehicle control. These challenges demand a more reliable design of automotive systems that is resilient to uncertainties and supports data integrity goals. Additionally, the increased connectivity of modern vehicles has made them highly vulnerable to various kinds of sophisticated security attacks. Hence, it is also vital to ensure the security of automotive systems, and it will become crucial as connected and autonomous vehicles become more ubiquitous. However, imposing security mechanisms on the resource constrained automotive systems can result in additional computation and communication overhead, potentially leading to further missed deadlines. Therefore, it is crucial to design techniques that incur very minimal overhead (lightweight) when trying to achieve the above-mentioned goals and ensure the real-time performance of the system. We address these issues by designing a holistic resource management framework called ROSETTA that enables robust and secure automotive cyber-physical system design while satisfying a diverse set of constraints related to reliability, security, real-time performance, and energy consumption. To achieve reliability goals, we have developed several techniques for reliability-aware scheduling and multi-level monitoring of signal integrity. To achieve security objectives, we have proposed a lightweight security framework that provides confidentiality and authenticity while meeting both security and real-time constraints. We have also introduced multiple deep learning based intrusion detection systems (IDS) to monitor and detect cyber-attacks in the in-vehicle network. Lastly, we have introduced novel techniques for jitter management and security management and deployed lightweight IDSs on resource constrained automotive ECUs while ensuring the real-time performance of the automotive systems

CAN With eXtensible In-Frame Reply: Protocol Definition and Prototype Implementation

Controller area network (CAN) has been the de facto standard in the automotive industry for the past two decades. Recently, CAN with flexible data-rate (CAN FD) has been standardized, which achieves noticeably higher throughput. Further improvements are still possible for CAN, by exploiting its peculiar physical layer to carry out distributed operations among network nodes, implemented as atomic transactions mapped on quasi-conventional frame exchanges. In this paper, a proposal is made for an extension to the CAN protocol, termed CAN with eXtensible in-frame Reply (CAN XR), which enables upper protocol layers to define new custom services devoted to, e.g., network management, application-specific functions, and high-efficiency data transfer. The key point is that CAN XR retains full backward compatibility with CAN, therefore, there is no need to change the protocol specification once again

A review on optimization techniques for the deployment and scheduling of distributed real-time systems

RESUMEN: En las ultimas tres décadas, se ha realizado un gran número de propuestas sobre la optimización del despliegue y planificación de sistemas de tiempo real distribuidos bajo diferentes enfoques algorítmicos que aportan soluciones aceptables a este problema catalogado como NP-difícil. En la actualidad, la mayor parte de los sistemas utilizados en el sector industrial son sistemas de criticidad mixta en los que se puede usar la planificación cíclica, las prioridades fijas y el particionado, que proporciona aislamiento temporal y espacial a las aplicaciones. Así, en este artículo se realiza una revisión de los trabajos publicados sobre este tema y se presenta un análisis de las diferentes soluciones aportadas para sistemas de tiempo real distribuidos basados en las políticas de planificación que se están usando en la práctica. Como resultado de la comparación, se presenta una tabla a modo de guía en la que se relacionan los trabajos revisados y se caracterizan sus soluciones.ABSTRACT: In the last three decades, a large number of proposals has been carried out for the optimization of the deployment and scheduling of distributed real-time systems under different algorithmic approaches that provide acceptable solutions for this NP-hard problem. Nowadays, most of the systems used in industry are mixed-criticallity systems which use cyclic scheduling, fixed-priority scheduling and partitioning, which provides both temporal and spatial isolation in the execution of applications. Thus, in this work a review of the works published on this topic is performed, as well as an analysis of the different proposed solutions for distributed real-time systems based on the scheduling policies that are used in practice. As a result of the comparison, a table intended as a guide is elaborated in which all the reviewed works are reported and their solutions are characterized.Este trabajo ha sido financiado en parte por el Gobierno de España y los fondos FEDER (AEI /FEDER, UE) en el proyecto TIN2017-86520-C3-3-R (PRECON-I4)

Mapping Requirements To AUTOSAR Software Components

Modern automotive electrical and electronic systems are rapidly growing in complexity. An increase in the number of systems under electronic control has led to a corresponding increase in the complexity of the deployed software. AUTOSAR has been developed as a means of managing this complexity through a standardised architecture which separates an application from its infrastructure. Reusable software components constitute the application logic of an AUTOSAR-based system. However a major problem which faces AUTOSAR and component-based software engineering in general is the difficulty in selecting components which fulfil the system requirements. This thesis presents a framework which allows requirements to be mapped directly to software components. It includes the results from a study which was carried out in conjunction with automotive and software engineering experts to test the framework

Schedulability analysis and optimization of time-partitioned distributed real-time systems

RESUMEN: La creciente complejidad de los sistemas de control modernos lleva a muchas empresas a tener que re-dimensionar o re-diseñar sus soluciones para adecuarlas a nuevas funcionalidades y requisitos. Un caso paradigmático de esta situación se ha dado en el sector ferroviario, donde la implementación de las aplicaciones de señalización se ha llevado a cabo empleando técnicas tradicionales que, si bien ahora mismo cumplen con los requisitos básicos, su rendimiento temporal y escalabilidad funcional son sustancialmente mejorables. A partir de las soluciones propuestas en esta tesis, además de contribuir a la validación de sistemas que requieren certificación de seguridad funcional, también se creará la tecnología base de análisis de planificabilidad y optimización de sistemas de tiempo real distribuidos generales y también basados en particionado temporal, que podrá ser aplicada en distintos entornos en los que los sistemas ciberfísicos juegan un rol clave, por ejemplo en aplicaciones de Industria 4.0, en los que pueden presentarse problemas similares en el futuro.ABSTRACT:he increasing complexity of modern control systems leads many companies to have to resize or redesign their solutions to adapt them to new functionalities and requirements. A paradigmatic case of this situation has occurred in the railway sector, where the implementation of signaling applications has been carried out using traditional techniques that, although they currently meet the basic requirements, their time performance and functional scalability can be substantially improved. From the solutions proposed in this thesis, besides contributing to the assessment of systems that require functional safety certification, the base technology for schedulability analysis and optimization of general as well as time-partitioned distributed real-time systems will be derived, which can be applied in different environments where cyber-physical systems play a key role, for example in Industry 4.0 applications, where similar problems may arise in the future

Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected