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We propose a novel model- and component-based technique to support communications-oriented development of software for vehicular distributed real-time embedded systems. The proposed technique supports modeling of legacy nodes and communication protocols by encapsulating and abstracting the internal implementation details and protocols. It also allows modeling and performing timing analysis of the applications that contain network traffic originating from outside of the system such as vehicle-to-vehicle, vehicle-to-infrastructure, and cloud-based applications. Furthermore, we present a method to extract end-to-end timing models to support end-to-end timing analysis. We also discuss and solve the issues involved during the extraction of these models. As a proof of concept, we implement our technique in the Rubus Component Model which is used for the development of software for vehicular embedded systems by several international companies. We also conduct an application-case study to validate our approach.

The existing worst-case response-time analysis for Controller Area Network (CAN) calculates upper bounds on the response times of messages that are queued for transmission either periodically or sporadically. However, it does not support the analysis of mixed messages. These messages do not exhibit a periodic activation pattern and can be queued for transmission both periodically and sporadically. They are implemented by several higher-level protocols based on CAN that are used in the automotive industry. We extend the existing analysis to support worst-case response-time calculations for periodic and sporadic as well as mixed messages. Moreover, we integrate the effect of hardware and software limitations in the CAN controllers and device drivers such as abortable and non-abortable transmit buffers with the extended analysis. The extended analysis is applicable to any higher-level protocol for CAN that uses periodic, sporadic and mixed transmission modes.

The existing worst case response-time analysis for controller area network (CAN) with nodesimplementing priority and First In First Out (FIFO) queues does not support mixed messages. It assumesthat a message is queued for transmission either periodically or sporadically. However, a message canalso be queued both periodically and sporadically using mixed transmission mode implemented by severalhigher level protocols for CAN that are used in the automotive industry. We extend the existing analysisfor CAN to support any higher level protocol for CAN that uses periodic, sporadic, and mixed transmissionof messages in the systems where some nodes implement priority queues, whereas others implement FIFOqueues. In order to provide a proof of concept, we implement the extended analysis in a free tool, conductan automotive-application case study, and perform comparative evaluation of the extended analysis with theexisting analysis.

The existing offset-based response-time analysis for mixed messages in Controller Area Network (CAN) assumes the jitter and deadline of a message to be smaller or equal to the transmission period. However, practical systems may contain messages whose release jitter and deadlines can be greater than their periods, e.g., in the gateway nodes. We extend the existing response-time analysis for mixed messages in CAN that are scheduled with offsets and have arbitrary jitter and deadlines. Mixed messages are implemented by several higher-level protocols for CAN that are used in the automotive industry. The extended analysis is applicable to any higher-level protocol for CAN that uses periodic, sporadic and mixed transmission modes.

We present a new response-time analyzer for Controller Area Network (CAN) that integrates and implements a number of response-time analyses which address various transmission modes and practical limitations in the CAN controllers. The existing tools for the response-time analysis of CAN support only periodic and sporadic messages. They do not analyze mixed messages which are partly periodic and partly sporadic. These messages are implemented by several higher-level protocols based on CAN that are used in the automotive industry. The new analyzer supports periodic, sporadic as well as mixed messages. It can analyze the systems where periodic and mixed messages are scheduled with offsets. It also supports the analysis of all types of messages while taking into account several queueing policies and buffer limitations in the CAN controllers such as abortable or non-abortable transmit buffers. Moreover, the tool supports the analysis of mixed, periodic and sporadic messages in the heterogeneous systems where Electronic Control Units (ECUs) implement different types of queueing policies and have different types of buffer limitations in the CAN controllers. We conduct a case study of a heterogeneous application from the automotive domain to show the usability of the tool. Moreover, we perform a detailed evaluation of the implemented analyses.

In this paper we discuss the implementation of the state-of-the-art end-to-end response-time and delay analysis as two individual plug-ins for the existing industrial tool suite Rubus-ICE. The tool suite is used for the development of software for vehicular embedded systems by several international companies. We describe and solve the problems encoun-tered and highlight the experiences gained during the process of imple-mentation, integration and evaluation of the analysis plug-ins. Finally, we provide a proof of concept by modeling the automotive-application case study with the existing industrial model (the Rubus Component Model), and analyzing it with the implemented analysis plug-ins.

In order to perform the end-to-end response-time and delay analyses of a system, its end-to-end timing model should be available. The majority of existing model- and component-based development approaches for vehicular distributed embedded systems extract the end-to-end timing model at an abstraction level and development phase that is close to the system implementation. We present a method to extract the end-to-end timing models from the systems at a higher abstraction level. At the higher level, the method extracts timing information from system models that are developed with EAST-ADL and Timing Augmented Description Language (TADL2) using the TIMMO methodology. At the lower level, the method exploits the Rubus component model to extract the timing information that cannot be clearly specified at the higher level such as trigger paths in distributed chains. We also discuss challenges and issues faced during extraction of the timing models. Further, we present guidelines and solutions to address these challenges.