Skip to main content
SpringerLink
Log in

Transparent integration of autonomous vehicles simulation tools with a data-centric middleware

  • Published:
Design Automation for Embedded Systems Aims and scope Submit manuscript

Abstract

Simulations are key steps in the design, implementation, and verification of autonomous vehicles (AV). Parallel to this, typical simulation tools fail to integrate the entirety of the aspects related to the complexity of AV applications, such as data communication delay, security, and the integration of software/hardware-in-the-loop and other simulation tools. This work proposes a SmartData-based middleware to integrate AV simulators and external tools. The interface models the data used on a simulator and creates an intermediary layer between the simulator and the external tools by defining the inputs and outputs as SmartData. A message bus is used for communication between SmartData following their Interest relations. Messages are exchanged following a specific protocol. Nevertheless, the architecture presented is agnostic of protocol. Moreover, we present a data-centric AV design integrated into the middleware. The design considers the standardization of the data interfaces between AV components, including sensing, perception, planning, decision, and actuation. Therefore, the presented design promotes a transparent integration of the AV simulation with other simulators (e.g., network simulators), cloud services, fault injection mechanisms, digital twins, and hardware-in-the-loop scenarios. Moreover, the design allows for transparent, runtime component replacement and time synchronization, the modularization of the vehicle components, and the addition of security aspects in the simulation. We present a case-study application with an AV simulation using CARLA, and we measure the end-to-end delay and overhead incurred in the simulation by our middleware. An increase in the end-to-end delay was measured once data communication was not acknowledged in the original scenario, and data was assumed to be ready for processing with no communication delay between sensors, decision-making, and actuation units.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The presented results do not include a replication dataset. To this purpose, the software developed is available for experiment replication at https://gitlab.lisha.ufsc.br/iot/smartdata-linux/-/tree/Transparent_Integration_of_AV-DAEM.

Notes

  1. The source code for the AV Design integrated with the SmartData middleware can be found at: https://gitlab.lisha.ufsc.br/iot/smartdata-linux/-/tree/Transparent_Integration_of_AV-DAEM.

  2. A full description of Digital UNIT encoding in SmartData is available online at https://epos.lisha.ufsc.br/EPOS+2+User+Guide#Unit.

  3. security flaws and attacks are out of the scope of this paper. See [7] and [10] for more details on V2X security.

References

  1. Ahmad N, Ghazilla RAR, Khairi NM et al (2013) Reviews on various inertial measurement unit (IMU) sensor applications. Int J Signal Process Syst. https://doi.org/10.12720/ijsps.1.2.256-262

    Article  Google Scholar 

  2. Althoff M, Magdici S (2016) Set-based prediction of traffic participants on arbitrary road networks. IEEE Trans Intell Veh 1(2):187–202. https://doi.org/10.1109/TIV.2016.2622920

    Article  Google Scholar 

  3. Bernstein DJ (2005) The poly1305-aes message-authentication code. In: Proceedings of fast software encryption, Paris, France, pp 32–49

  4. Calvo-Fullana M, Mox D, Pyattaev A et al (2021) Ros-netsim: a framework for the integration of robotic and network simulators. IEEE Robot Autom Lett 6(2):1120–1127. https://doi.org/10.1109/LRA.2021.3056347

    Article  Google Scholar 

  5. Codevilla F, Müller M, López A et al (2018) End-to-end driving via conditional imitation learning. In: 2018 IEEE international conference on robotics and automation (ICRA), pp 4693–4700

  6. Dosovitskiy A, Ros G, Codevilla F et al (2017) CARLA: an open urban driving simulator. In: Proceedings of the 1st annual conference on robot learning, pp 1–16

  7. de Lucena MM, Frohlich AA (2022) Modeling misbehavior detection timeliness in VANETs. In: 2022 IEEE 27th international conference on emerging technologies and factory automation (ETFA). IEEE. https://doi.org/10.1109/etfa52439.2022.9921605

  8. ETSI (2014) Intelligent transport systems (ITS); vehicular communications; basic set of applications; part 2: specification of cooperative awareness basic service. Standard, European Standard (Telecommunications series), France

  9. ETSI (2019) Intelligent transport systems (ITS); vehicular communications; basic set of applications; analysis of the collective perception service (cps); release 2. Standard, European Standard (Telecommunications series), France

  10. ETSI (2021) Intelligent transport systems (ITS). Security; trust and privacy management, standard, European Standard (Telecommunications series), France

  11. Even R (2006) RTP payload format for H.261 video streams. RFC 4587. https://doi.org/10.17487/RFC4587

  12. Fröhlich AA (2018) SmartData: an IoT-ready API for sensor networks. Int J Sens Netw 28(3):202

    Article  Google Scholar 

  13. Huang W, Wang K, Lv Y et al (2016) Autonomous vehicles testing methods review. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), pp 163–168. https://doi.org/10.1109/ITSC.2016.7795548

  14. IEC (2019) Industrial communication networks—fieldbus specifications—part 1: overview and guidance for the IEC 61158 and IEC 61784 series. Technical report. International Electrotechnical Commission, Geneva, Switzerland

  15. Instrumentation I, Society M (2007) IEEE standard for a smart transducer interface for sensors and actuators-common functions, communication protocols, and transducer electronic data sheet (teds) formats. IEEE Std 14510-2007, pp 1–335. https://doi.org/10.1109/IEEESTD.2007.4338161

  16. Kamel J, Ansari MR, Petit J et al (2020) Simulation framework for misbehavior detection in vehicular networks. IEEE Trans Veh Technol 69(6):6631–6643. https://doi.org/10.1109/TVT.2020.2984878

    Article  Google Scholar 

  17. Liu S, Li L, Tang J et al (2020) Creating autonomous vehicle systems. Springer, Berlin. https://doi.org/10.1007/978-3-031-01805-3

    Book  Google Scholar 

  18. Lopez PA, Behrisch M, Bieker-Walz L et al (2018) Microscopic traffic simulation using sumo. In: The 21st IEEE international conference on intelligent transportation systems. IEEE. https://elib.dlr.de/124092/

  19. Pereira JLF, Rossetti RJF (2012) An integrated architecture for autonomous vehicles simulation. In: Proceedings of the 27th annual ACM symposium on applied computing. association for computing machinery, New York, NY, USA, SAC ’12, pp 286–292. https://doi.org/10.1145/2245276.2245333

  20. Resner D, Fröhlich AA (2015) Design rationale of a cross-layer, trustful space-time protocol for wireless sensor networks. In: 2015 IEEE 20th conference on emerging technologies & factory automation (ETFA). IEEE. https://doi.org/10.1109/etfa.2015.7301413

  21. Richa CH, de Lucena MM, Horstmann LP et al (2020) Modeling time requirements of CPS in wireless networks. Sensors 20(7):1818. https://doi.org/10.3390/s20071818

    Article  Google Scholar 

  22. Shah S, Dey D, Lovett C et al (2018) Airsim: high-fidelity visual and physical simulation for autonomous vehicles. In: Hutter M, Siegwart R (eds) Field and service robotics. Springer, Cham, pp 621–635

    Chapter  Google Scholar 

  23. Stewart P, McCanne S, Fenner B et al (1998) RTP payload format for JPEG-compressed video. RFC 2435. https://doi.org/10.17487/RFC2435

  24. Tian Y, Pei K, Jana S et al (2018) Deeptest: automated testing of deep-neural-network-driven autonomous cars. In: 2018 IEEE/ACM 40th international conference on software engineering (ICSE), pp 303–314. https://doi.org/10.1145/3180155.3180220

  25. Wang YK, Sanchez Y, Schierl T et al (2016) RTP payload format for high efficiency video coding (HEVC). RFC 7798. https://doi.org/10.17487/RFC7798

Download references

Author information

Authors and Affiliations

  1. Software/Hardware Integration Lab, Federal University of Santa Catarina, Campus Universitário Reitor João David Ferreira Lima, Trindade, Florianópolis, Santa Catarina, 88040-900, Brazil

    José Luis Conradi Hoffmann, Leonardo Passig Horstmann & Antônio Augusto Fröhlich

Authors
  1. José Luis Conradi Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonardo Passig Horstmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antônio Augusto Fröhlich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Luis Conradi Hoffmann.

Ethics declarations

Conflict of interest

The authors have declared no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This manuscript is an extended version of the conference paper that appeared in https://doi.org/10.1109/SBESC56799.2022.9964834. This work was partially supported by Fundação de Desenvolvimento da Pesquisa—Fundep Rota 2030/Linha V 27192.02.01/2020.09-00. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Conradi Hoffmann, J.L., Passig Horstmann, L. & Fröhlich, A.A. Transparent integration of autonomous vehicles simulation tools with a data-centric middleware. Des Autom Embed Syst (2024). https://doi.org/10.1007/s10617-023-09280-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10617-023-09280-w

Keywords

Search

Navigation