Enabling Time Synchronization with Hardware-in-the-Loop Integration on a Data-Driven Middleware for Autonomous Vehicles Simulations

• Ilton Pfleger Júnior UFSC http://orcid.org/0009-0001-1730-8330
• Leonardo Passig Horstmann UFSC https://orcid.org/0000-0002-3581-275X
• Antônio A. Fröhlich UFSC https://orcid.org/0000-0002-4063-1339

DOI: https://doi.org/10.5753/ssv.2024.32620

Resumo

Building reliable simulation scenarios and digital twins is a keystone for the development process of critical systems such as autonomous vehicles. In this sense, the ability to integrate simulators with software-, hardware-, and even vehicles-in-the-loop is fundamental for increasing the reliability of the simulations. Nevertheless, guaranteeing time synchronization amongst the different components of the system is a challenging task that must consider the different capabilities regarding timing in each device. In this work, we build upon a data-driven middleware to provide transparent hardware-in-the-loop integration to an autonomous vehicle simulation scenario and extend the original implementation to account for time synchronization by taking advantage of a high-precision time source provided by the hardware components. We operate the middleware in Linux over a NVIDIA Jetson Orin AGX, measuring an average deviation of 6ms when compared to the high-precision hardware-in-the-loop timestamps, with the deviation exceeding the established safe margin over 69% of the observations. The attained results corroborate the need for time synchronization on digital twins, especially when considering the strict timing requirements of critical systems such as autonomous vehicles.

Palavras-chave: digital-twins, hardware-in-the-loop, time synchronization

Referências

Felipe Codevilla, Matthias Muller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy. End-to-end driving via conditional imitation learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 4693–4700, 2018.

Jose Luis Conradi Hoffmann, Leonardo Passig Horstmann, and Antonio Augusto Fröhlich. Transparent integration of autonomous vehicles simulation tools with a data-centric middleware. Design Automation for Embedded Systems, 28(1):45–66, January 2024.

Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1–16, 2017.

Antonio Augusto Fröhlich. SmartData: an IoT-ready API for sensor networks. International Journal of Sensor Networks, 28(3):202, 2018.

Jose Luis Conradi Hoffmann, Leonardo Passig Horstmann, and Antonio Augusto Fröhlich. Integrating autonomous vehicle simulation tools using smartdata. In 2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC), page 1–8. IEEE, November 2022.

WuLing Huang, Kunfeng Wang, Yisheng Lv, and FengHua Zhu. Autonomous vehicles testing methods review. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 163–168, 2016.

IEEE Instrumentation and Measurement Society. Ieee standard for a smart transducer interface for sensors and actuators - common functions, communication protocols, and transducer electronic data sheet (teds) formats. IEEE Std 1451.0-2007, pages 1–335, 2007.

Open Robotics. Gazebo.