The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

IMU Sensor Emulator for Autonomous Driving Simulator

자율주행 드라이빙 시뮬레이터용 IMU 센서 에뮬레이터

  • Jae-Un Lee (Dept. of Electrical and Computer Engineering, Inha University) ;
  • Dong-Hyuk Park (Dept. of Electrical and Computer Engineering, Inha University) ;
  • Jong-Hoon Won (Dept. of Electrical and Computer Engineering, Inha University)
  • 이재운 (인하대학교 전기컴퓨터공학과) ;
  • 박동혁 (인하대학교 전기컴퓨터공학과) ;
  • 원종훈 (인하대학교 전기공학과)
  • Received : 2023.11.24
  • Accepted : 2023.12.15
  • Published : 2024.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

자율주행 기술 개발에 있어서 드라이빙 시뮬레이터의 활용은 사고 환경에 대한 다양한 테스트를 단시간에 효과적으로 수행 가능하게 하여 개발 비용 및 노력을 획기적으로 줄일 수 있다. 그러나 드라이빙 시뮬레이터는 실제 환경과 차이가 존재한다는 심각한 단점이 존재하므로 드라이빙 시뮬레이터를 이용하여 개발된 자율주행 알고리즘이 실제 차량 시스템에 직접 적용시킬 때 큰 차이가 발생하는 문제가 있다. 이러한 문제는 Sim2Real 문제로 정의되며, 시나리오, 센서 모델링, 차량 동역학 등으로 구분할 수 있다. 본 논문에서는 Sim2Real 문제 중에서 IMU센서에 대해서 Sim2Real 문제 해결 방안에 대한 연구를 수행한다. 실제 환경과 에뮬레이터된 가상 IMU 센서의 차이를 줄이기 위해서 IMU 센서의 정밀 오차 모델링을 통한 센서 에뮬레이터 기술에 대해 소개한다. Bias, Scale Factor, Misalignmnet, Random Walk에 따른 오차를 가상 IMU 센서 등급별로 모델링하고 이를 실제 IMU 센서의 등급에 따른 오차 지표와 비교함으로써 IMU 센서의 Sim2Real 문제를 완화한다.

Keywords

Acknowledgement

이 논문은 2023년도 정부(경찰청)의 재원으로 과학치안진흥센터의 지원을 받아 수행된 연구임 (No.092021D75000000, 자율주행기술개발혁신사업).

References

  1. Analog Devices, https://www.analog.com/media/en/technical-documentation, 2023.10.40.
  2. Bhatti, G., Mohan, H. and Singh, R. R.(2021), "Towards the future of smart electric vehicles: Digital twin technology", Renewable and Sustainable Energy Reviews, vol. 141, no. 110801.
  3. Bochkati, M.(2014), Simulation and Processing of IMU Observations based on various Sensor Characteristics.
  4. Bochkati, M., Schon, S., Schlippert, D., Schubert, C. and Rasel, E.(2017), "Could cold atom interferometry sensors be the future inertial sensors?-First simulation results", 2017 DGON Inertial Sensors and Systems(ISS), IEEE, pp.1-20.
  5. Cheng, J., Fang, J., Wu, W. and Li, J.(2014), "Temperature drift modeling and compensation of RLG based on PSO tuning SVM", Measurement, vol. 55, pp.246-254. https://doi.org/10.1016/j.measurement.2014.05.013
  6. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A. and Koltun, V.(2017), "CARLA: An open urban driving simulator", Conference on Robot Learning, pp.1-16.
  7. Honeywell, https://aerospace.honeywell.com/us/en/products-and-services/product/hardware-and-systems/sensors/hg5700-inertial-measurement-unit, 2023.10.40.
  8. Itterton, D. and Weston, J. L.(2004), Strapdown inertial navigation technology, IET, p.127.
  9. Jekeli, C.(1983), Inertial Navigation Systems with Geodetic Applications, Walter de Gruyter GmbH & Co KG, p.171.
  10. Lee, S. Y., Shim, Y. B., Park, C. G. and Min, K. W.(2021), "Autonomous driving simulation SW technology for reality-virtual information convergence", Proceedings of the Korea Electronics Technology Institute, Jeju, Korea, pp.407-408.
  11. Microstrain, https://www.microstrain.com/inertial-sensors/3dm-gx5-25, 2023.10.04.
  12. MORAI, https://help-morai-sim.scrollhelp.site/morai-sim-standard-kr/sensor-noise, 2023a.11.13.
  13. MORAI, https://www.morai.ai/ko, 2023b.11.13.
  14. Movella, https://www.movella.com/products/sensor-modules/xsens-mti-100-imu, 2023.10.04.
  15. Novatel, https://novatel.com/products/gnss-inertial-navigation-systems/imus/p-1750, 2023.10.04.
  16. Park, D. H. and Won, J. H.(2022), "An Interim Result of the Study on Co-Simulation Method for Testing Autonomous Driving Algorithms", The Korea Institute of Intelligent Transport Systems International Conference 2022, ICC JEJU, Korea, pp.873-876.
  17. Prikhodko, I. P., Bearss, B., Merritt, C., Bergeron, J. and Blackmer, C.(2018), "Towards self-navigating cars using MEMS IMU: Challenges and opportunities", 2018 IEEE International Symposium on Inertial Sensors and Systems(INERTIAL), IEEE, pp.1-4.
  18. Rong, G., Shin, B. H., Tabatabaee, H., Lu, Q., Lemke, S., Mozeiko, M. and Kim, S.(2020), "Lgsvl simulator: A high fidelity simulator for autonomous driving", 2020 IEEE 23rd International Conference on Intelligent Transportation Systems(ITSC), pp.1-6.
  19. Roth, E., Dirndorfer, T., Neumann-Cosel, K. V., Fischer, M. O., Ganslmeier, T., Kern, A. and Knoll, A.(2011), "Analysis and validation of perception sensor models in an integrated vehicle and environment simulation", Proceedings of the 22nd Enhanced Safety of Vehicles Conference, pp.1-9.
  20. Shah, S., Dey, D., Lovett, C. and Kapoor, A.(2018), "Airsim: High-fidelity visual and physical simulation for autonomous vehicles", In Field and Service Robotics, pp.621-635.
  21. Siouris, G.(1993), Aerospace avionics systems: A modern synthesis, Academic Press.
  22. Tanil, C.(2016), Detecting GNSS spoofing attacks using INS coupling, Illinois Institute of Technology.
  23. Titterton, D. H. and Weston, J. L.(2004), Strapdown inertial navigation technology (2nd ed.), Institution of Electrical Engineers.
  24. Vectornav, https://www.vectornav.com/resources/inertial-navigation-articles/what-is-an-ins, 2023.10.04.
  25. Xu, B., Wang, L. and Duan, T.(2019), "A novel hybrid calibration method for FOG-based IMU", Measurement, vol. 147, no. 106900.
  26. Yang, Y., El-Sheimy, N., Goodall, C. and Niu, X.(2007), "IMU signal software simulator", Proceedings of the 2007 National Technical Meeting of The Institute of Navigation, San Diego, CA, pp.532-538.