Model-Based Design Approach for Validation of Vehicle Sensor Fusion Algorithm

Authors

  • Ashley Phan The University of Alabama
  • Brandon Stevens The University of Alabama
  • Benjamin Fitzpatrick The University of Alabama
  • Jordan Olson The University of Alabama
  • Hwan-Sik Yoon The University of Alabama https://orcid.org/0000-0001-8619-1693
  • Paul Puzinauskas The University of Alabama

DOI:

https://doi.org/10.47611/jsr.v12i3.1943

Keywords:

Connected and autonomous vehicle, sensor fusion, model-in-the-loop testing, hardware-in-the-loop testing

Abstract

In recent years, the development of connected and autonomous vehicle (CAV) systems has accelerated dramatically as these systems can provide considerable benefits for vehicle performance and passenger safety. However, CAV systems can prove hazardous if not properly tested before deployment. A need for testing methods that are economical, time-effective, and low-risk has created the concept of model-based design, whereby systems are modeled and tested in various simulation environments before being fully deployed to the real system. This paper describes a model-based testing approach that has been developed to verify and validate a sensor fusion algorithm for CAV systems to enhance autonomous controls of a 2019 Chevrolet Blazer as part of the EcoCAR Mobility Challenge (EMC). Teams of undergraduate and graduate students with automotive interests laid out testing procedures to assess model fidelity and to identify and resolve issues with the algorithm before deployment to a student-adapted prototype vehicle. Students learned and applied complex engineering concepts rapidly to develop a sensor fusion and tracking algorithm.

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References or Bibliography

General Motors. 2021. “General Motors forges ahead toward a future of Zero Crashes, Zero Emissions and Zero Congestion at CES 2021.” Accessed January 31, 2022. https://media.gm.com/media/me/en/gm/news.detail.html/content/Pages/news/me/en/2021/gm/01-19-General-Motors-forges-ahead-toward-a-future-of-Zero-Crashes-Zero-Emissions-and-Zero-Congestion-at-CES-2021.html.

Gindrat, R. 2018. “How MaaS Public Transit Is Changing the World.” Accessed January 31, 2022. https://www.forbes.com/sites/forbestechcouncil/2018/11/06/how-maas-public-transit-is-changing-the-world/?sh=509683497518.

MarketsandMarkets. 2021. “Mobility as a Service Market.” Accessed February 1, 2022. https://www.marketsandmarkets.com/Market-Reports/mobility-as-a-service-market-78519888.html.

Britt J, Shoults LW. How COVID-19 led to improvements and adaptations to experiential learning opportunities for an increasingly remote environment. In2021 ASEE Virtual Annual Conference Content Access 2021 Jul 26.

SAE International. 2018. "SAE International Releases Updated Visual Chart For Its ‘Levels Of Driving Automation’ Standard For Self-Driving Vehicles.” Accessed February 1, 2022. https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles.

Marsden G, McDonald M, Brackstone M. Towards an understanding of adaptive cruise control. Transportation Research Part C: Emerging Technologies. 2001 Feb 1;9(1):33-51.

Xiao L, Gao F. A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics. 2010 Oct 1;48(10):1167-92.

Yeong DJ, Velasco-Hernandez G, Barry J, Walsh J. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors. 2021 Mar 18;21(6):2140.

Kim J, Han DS, Senouci B. Radar and vision sensor fusion for object detection in autonomous vehicle surroundings. In2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) 2018 Jul 3 (pp. 76-78). IEEE.

Aarenstrup, R. 2015. Managing Model-Based Design. Natick: The MathWorks.

Mahapatra S, Egel T, Hassan R, Shenoy R, Carone M. Model-based design for hybrid electric vehicle systems. SAE International; 2008 Apr 14.

Plummer AR. Model-in-the-loop testing. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2006 May 1;220(3):183-99.

Pietruch M, Mlyniec A, Wetula A. An overview and review of testing methods for the verification and validation of ADAS, active safety systems, and autonomous driving. Mining–Informatics, Automation and Electrical Engineering. 2020;58.

Olson J, Stevens B, Phan A, Yoon HS, Puzinauskas P. Model-based design approach for validation of vehicle longitudinal control algorithm. Journal of student research. 2022 Jun 12;11(2).

Park YS, Choi WS, Han SI, Lee JM. Navigation system of UUV using multi-sensor fusion-based EKF. Journal of institute of control, robotics and systems. 2016;22(7):562-9.

Yeong DJ, Velasco-Hernandez G, Barry J, Walsh J. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors. 2021 Mar 18;21(6):2140.

Published

08-31-2023

How to Cite

Phan, A., Stevens, B., Fitzpatrick, B., Olson, J., Yoon, H.-S., & Puzinauskas, P. (2023). Model-Based Design Approach for Validation of Vehicle Sensor Fusion Algorithm. Journal of Student Research, 12(3). https://doi.org/10.47611/jsr.v12i3.1943

Issue

Section

Research Articles