Computer Science ›› 2018, Vol. 45 ›› Issue (4): 190-195, 226.doi: 10.11896/j.issn.1002-137X.2018.04.032

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Study on Automatic Method for AUTOSAR Runnable Entity-task Mapping

RAN Zheng, LUO Lei, YAN Hua and LI Yun   

  • Online:2018-04-15 Published:2018-05-11

Abstract: The next generation automotive electronic standard AUTOSAR defines that the automotive application design process includes system level design and ECU level design.Software components are function units of application in system level design and each software component comprises a set of runnable entities.The main task of ECU level design is organizing the code segments of runnable entities as embedded operating system tasks.In the process of transforming the component set which is assigned from one ECU into a real-time system task set,the experienced embedded development engineers are necessary for runnable entity-task mapping configuration to ensure the real-time performance of the system.As the requirements of runnable entity-task mapping configuration are large and complex,this paper proposed a runnable entity-task automatic mapping method.With the consideration of trigger relationship between runnable entities,period requirements,data sharing and other factors,this method has important practical significance in improving the efficiency of automotive software development.Finally,the proposed method was applied to the automotive electronic cruise control system instance in AUTOSAR.The experimental results show that the proposed method has good performance in the aspects of jitter time,blocking time,frequency of scheduling and data traffic.

Key words: Automotive electronics,ECU configuration,Runnable entity,Task,Mapping

[1] FRST S,BECHTER M.AUTOSAR for Connected and Au-tonomous Vehicles:The AUTOSAR Adaptive Platform [C]∥46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop.2016:215-217.
[2] PARTNERSHIP A.Specification of Operating System(V4.1.0R4.0 Rev 2).http://www.autosar.org.2010.
[3] ZHANG M,GU Z H.Optimization Issues in Mapping AUTOSAR Components To Distributed Multithreaded Implementations[C]∥22nd IEEE International Symposium on Rapid System Prototyping(RSP).2011:23-29.
[4] LONG R S,LI H,PENG W,et al.An Approach to Optimize Intra-ECU Communication Based on Mapping of AUTOSAR Runnable Entities [C]∥International Conference on Embedded Software and Systems.2009:138-143.
[5] PARTNERSHIP A.Specification of RTEV(3.1.0R4.0 Rev 2) .http://www.autosar.org.2010.
[6] HU M L,LUO J,WANG Y,et al.Scheduling periodic taskgraphs for safety-critical time-triggered avionic systems [J].IEEE Transactions on Aerospace and Electronic Systems,2015,1(3):2294-2304.
[7] XIE G Q,ZENG G,LI Z T,et al.Adaptive Dynamic Scheduling on Multi-functional Mixed-Criticality Automotive Cyber-Physical Systems [J].IEEE Transactions on Vehicular Technology,2017(99):1-15.
[8] KAI R.Compositional Scheduling AnalysisUsing StandardEvent Models [D].Braunschweig:Technical University Carolo-Wilhelmina of Braunschweig,2005.
[9] MONOT A,NAVET N,BAVOUX B,et al.Multisource Software on Multicore Automotive ECUs-Combining Runnable Sequencing With Task [J].Scheduling IEEE Transactions on Industrial Electronics,2012,59(10):3934-3942.
[10] FERRARI A,NATALE M D,GENTILE G,et al.Time andmemory tradeoffs in the implementationof AUTOSAR components [C]∥Conference on Design,Automation and Test in Europe.2009:864-869.
[11] FARAGARDI H R,LISPER B,SANDSTRM K,et al.A Communication-Aware Solution Framework for MappingAUTOSAR Runnables on Multi-core Systems [C]∥Proceedings of the 2014 IEEE Emerging Technology and Factory Automation.2014:1-9.
[12] HAN J W,KAMBER M,PEI J.Data Mining:Concepts andTechniques(Third Edition)[M].Burlington:Morgan Kaufmann.2011:456-461.
[13] PARTNERSHIP A.Explanation of Application Interfaces of the Body and Comfort Domain (V1.2.0 R4.0 Rev 2) .http://www.autosar.org,2010.

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