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Deterministic and Reliable Software-Defined Vehicles: key building blocks, challenges, and vision

Pedro Veloso Teixeira, Duarte Raposo, Rui Lopes, Susana Sargento

TL;DR

The paradigm of Deterministic Software Defined Vehicles is explored, aiming to enhance the quality and ease of developing automotive services by focusing on service-oriented architectures, virtualization techniques, and the necessary deterministic intra- and inter-vehicular communications.

Abstract

As vehicle systems become increasingly complex, with more features, services, sensors, actuators, and processing units, it is important to view vehicles not just as modes of transportation moving toward full autonomy, but also as adaptive systems that respond to the needs of their occupants. Vehicular services can be developed to support these adaptations. However, the increasing complexity of vehicular service development, even with current standardizations, best practices and guidelines, are insufficient to tackle the high complexity of development, with expectations of up to 1 (U.S.) billion lines of code for a fully (level 5) autonomous vehicle. Within this survey, the paradigm of Deterministic Software Defined Vehicles is explored, aiming to enhance the quality and ease of developing automotive services by focusing on service-oriented architectures, virtualization techniques, and the necessary deterministic intra- and inter-vehicular communications. Considering the main open challenges for such verticals, a vision architecture towards improved services development and orchestration is presented, focusing on: a) a deterministic network configurator; b) a data layer configurator; c) a hypervisor configurator; d) the vehicle abstraction layer; and e) a software orchestrator.

Deterministic and Reliable Software-Defined Vehicles: key building blocks, challenges, and vision

TL;DR

The paradigm of Deterministic Software Defined Vehicles is explored, aiming to enhance the quality and ease of developing automotive services by focusing on service-oriented architectures, virtualization techniques, and the necessary deterministic intra- and inter-vehicular communications.

Abstract

As vehicle systems become increasingly complex, with more features, services, sensors, actuators, and processing units, it is important to view vehicles not just as modes of transportation moving toward full autonomy, but also as adaptive systems that respond to the needs of their occupants. Vehicular services can be developed to support these adaptations. However, the increasing complexity of vehicular service development, even with current standardizations, best practices and guidelines, are insufficient to tackle the high complexity of development, with expectations of up to 1 (U.S.) billion lines of code for a fully (level 5) autonomous vehicle. Within this survey, the paradigm of Deterministic Software Defined Vehicles is explored, aiming to enhance the quality and ease of developing automotive services by focusing on service-oriented architectures, virtualization techniques, and the necessary deterministic intra- and inter-vehicular communications. Considering the main open challenges for such verticals, a vision architecture towards improved services development and orchestration is presented, focusing on: a) a deterministic network configurator; b) a data layer configurator; c) a hypervisor configurator; d) the vehicle abstraction layer; and e) a software orchestrator.
Paper Structure (34 sections, 25 figures, 8 tables)

This paper contains 34 sections, 25 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Software Defined Vehicles
  3. Industrial landscape
  4. Main building blocks for Software Defined Vehicles
  5. Physical E/E architectures
  6. Service-oriented architectures
  7. Vehicular operating systems
  8. Virtualization and orchestration techniques
  9. Intra-vehicular communications
  10. Inter-vehicular communications
  11. Deterministic intra and inter-vehicular communications
  12. In wired context
  13. In wireless context
  14. In vehicular context
  15. Open challenges
  16. ...and 19 more sections

Figures (25)

  • Figure 1: Evolution of complexity in vehicular architectures 6183198.
  • Figure 2: Average complexity of individual software projects grown by 300 percent over the past decade, creating an unsustainable gap between software complexity and the productivity of software developers 2022-Problem-8Thecasef27:online.
  • Figure 3: Evolution of vehicular features over time, including levels of *sdv. Partially based on moritzhistoryOfCars.
  • Figure 4: Preferred payments scheme to purchase/activate connectivity services, % of respondents (n = 1649) showcasing a shift in preferred ownership payment models mckinsey.
  • Figure 5: Positioning of main automakers and frameworks over the overall industrial landscape.
  • ...and 20 more figures