Application on Automotive Cyber-Physical Systems

  • Guoqi Xie
  • Gang Zeng
  • Renfa Li
  • Keqin Li


In modern automobiles, multiple automotive applications with different criticality levels can be executed on one electronic control unit (ECU), and one application can be executed in parallel on multiple ECUs in the integrated automotive architecture. Considering the inherent heterogeneity, interaction, and diverse nature of such an architecture, automotive embedded systems have evolved to ACPS, which consist of multiple parallel automotive applications with different criticality levels. Efficient scheduling strategies can fully utilize ECUs on ACPS for high performance. However, ACPS should deal with joint challenges of heterogeneity, dynamics, parallelism, safety, and criticality, and these challenges are the key issues that will be solved in the next generation AUTOSAR adaptive platform. This chapter first proposes a fairness-based dynamic scheduling algorithm FDS_MIMF to minimize the individual schedule lengths of applications from a high performance perspective. FDS_MIMF can respond autonomously to the joint challenges of heterogeneity, dynamics, and parallelism of ACPS. To further respond autonomously to the joint challenges of heterogeneity, dynamics, parallelism, safety, and criticality of ACPS, we present an adaptive dynamic scheduling algorithm ADS_MIMF to achieve low deadline miss ratios (DMRs) of safety-critical applications from a timing constraint perspective while maintaining the acceptable overall schedule length of ACPS from a high performance perspective. ADS_MIMF is implemented by raising and reducing the criticality level of ACPS to adjust the execution of different applications on different criticality levels without increasing the time complexity. Finally, this chapter identifies that FDS_MIMF can obtain short overall makespan, whereas ADS_MIMF can reduce the DMR values of high-criticality functions while still keeping satisfactory performance of ACPS using typical experiments.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Guoqi Xie
    • 1
  • Gang Zeng
    • 2
  • Renfa Li
    • 3
  • Keqin Li
    • 4
  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  2. 2.Graduate School of EngineeringNagoya UniversityNagoyaJapan
  3. 3.Key Laboratory for Embedded and Cyber-Physical Systems of Hunan ProvinceHunan UniversityChangshaChina
  4. 4.Department of Computer ScienceState University of New YorkNew PaltzUSA

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