Controller Area Network (CAN) is used extensively in automotive applications, with in excess of 400 million CAN enabled microcontrollers manufactured each year. In 1994 schedulability analysis was developed for CAN, showing how worst-case response times of CAN messages could be calculated and hence guarantees provided that message response times would not exceed their deadlines. This seminal research has been cited in over 200 subsequent papers and transferred to industry in the form of commercial CAN schedulability analysis tools. These tools have been used by a large number of major automotive manufacturers in the design of in-vehicle networks for a wide range of cars, millions of which have been manufactured during the last decade. This paper shows that the original schedulability analysis given for CAN messages is flawed. It may provide guarantees for messages that will in fact miss their deadlines in the worst-case. This paper provides revised analysis resolving the problems with the original approach. Further, it highlights that the priority assignment policy, previously claimed to be optimal for CAN, is not in fact optimal and cites a method of obtaining an optimal priority ordering that is applicable to CAN. The paper discusses the possible impact on commercial CAN systems designed and developed using flawed schedulability analysis and makes recommendations for the revision of CAN schedulability analysis tools. R. I. Davis ( ) . A. Burns

Fixed-priority scheduling with deferred preemption (FPDS) has been proposed in the literature as a viable alternative to fixed-priority pre-emptive scheduling (FPPS), that obviates the need for non-trivial resource access protocols and reduces the cost of arbitrary preemptions. This paper shows that existing worst-case response time analysis of hard real-time tasks under FPDS, arbitrary phasing and relative deadlines at most equal to periods is pessimistic and/or optimistic. The same problem also arises for fixed-priority nonpre-emptive scheduling (FPNS), being a special case of FPDS. This paper provides a revised analysis, resolving the problems with the existing approaches. The analysis is based on known concepts of critical instant and busy period for FPPS. To accommodate for our scheduling model for FPDS, we need to slightly modify existing definitions of these concepts. The analysis assumes a continuous scheduling model, which is based on a partitioning of the timeline in a set of non-empty, right semi-open intervals. It is shown that the critical instant, longest busy period, and worst-case response time for a task are suprema rather than maxima for all tasks, except for the lowest priority task. Hence, that instant, period, and response time cannot be assumed for any task, except for the lowest priority task. Moreover, it is shown that the analysis is not uniform for all tasks, i.e. the analysis for the lowest priority task differs from the Real-Time Syst (2009) 42: 63-119 analysis of the other tasks. These anomalies for the lowest priority task are an immediate consequence of the fact that only the lowest priority task cannot be blocked. To build on earlier work, the worst-case response time analysis for FPDS is expressed in terms of known worst-case analysis results for FPPS. The paper includes pessimistic variants of the analysis, which are uniform for all tasks, illustrates the revised analysis for an advanced model for FPDS, where tasks are structured as flow graphs of subjobs rather than sequences, and shows that our analysis is sustainable.

Fixed-priority scheduling with deferred preemption (FPDS)

Video processing in software is often characterized by highly fluctuating, content-dependent processing times, and a limited tolerance for deadline misses. We present an approach that allows close-to-average-case resource allocation to a single video processing task, based on asynchronous, scalable processing, and QoS adaptation. The QoS adaptation balances different QoS parameters that can be tuned, based on user-perception experiments: picture quality, deadline misses, and quality changes. We model the balancing problem as a discrete stochastic decision problem, and propose two solution strategies, based on a Markov decision process and reinforcement learning, respectively. We enhance both strategies with a compensation for structural (non-stochastic) load fluctuations. Finally, we validate our approach by means of simulation experiments, and conclude that both enhanced strategies perform close to the theoretical optimum.

1.1. HVE consumer products +9( FRQVXPHU SURGXFWV DUH KHDYLO\ FRVW FRQVWUDLQHG 7KHUHIRUH WKH DYDLODEOH UHVRXUFHV PXVW EH XVHG YHU\ FRVW HIIHFWLYHO\ ZKLOH SUHVHUYLQJ W\SLFDO TXDOLWLHV RI +9( FRQVXPHU WHUPLQDOV VXFK DV UREXVWQHVV DQG PHHWLQJ VWULQJHQW WLPLQJ UHTXLUHPHQWV LPSRVHG E\ KLJKTXDOLW\ GLJLWDO DXGLR DQG YLGHR SURFHVVLQJ 0RUHRYHU perception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any real-time systems needing an on-line schedulability test require exact schedulability analysis. In this paper we evaluate standard initial values for the iterative procedure to calculate worst-case response times of periodic tasks under fixed priority preemptive scheduling and arbitrary phasing. For discrete scheduling, we show that the number of iterations needed to determine the worst-case response time of a task using standard initial values increases logarithmically for an increasing worst-case computation time of that task. We present a new initial value, and prove that the number of iterations for that value is bounded. The costs of using the standard and new initial values are compared by means of an experiment. We briefly discuss the applicability of the initial value in other contexts, such as best-case response time analysis and jitter analysis.

Fixed-priority scheduling with deferred preemption (FPDS) and fixed-priority scheduling with preemption thresholds (FPTS) have been proposed in the literature as viable alternatives to fixed-priority preemptive scheduling (FPPS), that reduce memory requirements, reduce the cost of arbitrary preemptions, and may improve the feasibility of a task set even when preemption overheads are neglected.This paper aims at advancing the relative strength of limitedpreemptive schedulers by combining FPDS and FPTS. In particular, we present a refinement of FPDS with preemption thresholds for both jobs and sub-jobs, termed FPGS. We provide an exact schedulability analysis for FPGS, and show how to maximize the feasibility of a set of sporadic tasks under FPGS for given priorities, computation times, periods, and deadlines of tasks. We evaluate the effectiveness of FPGS by comparing the feasibility of task sets under FPGS with other fixed-priority scheduling algorithms by means of a simulation. Our experiments show that FPGS allows an increase of the number of task sets that are schedulable under fixed-priority scheduling.

No abstract

Abstract-Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension towards maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lanereduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving.Index Terms-Cooperative driving, interaction protocol, controller design, vehicle platoons, wireless communications. A. Voronov and C. Englund are with RISE Viktoria,