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AEB Simulation Method Based on Vehicle Test Data for Accident Analysis of ADAS Vehicles

  • Vehicle Dynamics and Control, Other Fields of Automotive Engineering
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Abstract

The use of vehicles equipped with autonomous emergency braking (AEB), among the most widely employed functions of the advanced driver assistance system (ADAS), is rising, and thus so is demand for analytical services for accident investigation related to AEB issues. In fact, even if the AEB system functions properly, accidents may still occur due to various causes, such as the malfunction of ADAS sensors, functional or performance limits of ADAS operating algorithms, or driver negligence. Therefore, a clear understanding of the workings of ADAS sensors and ADAS operating algorithms, as well as analytical tools and scientific methods for accident reconstruction and forensic analysis, is required to ensure accuracy and consistency in accident investigation. The present study focused on the reconstruction and analysis of traffic accidents involving autonomous and ADAS vehicles. To this end, an attempt was made to create an AEB simulation environment in which various AEB functions can be implemented and tested. AEB function tests were conducted according to the EuroNCAP AEB's test scenarios to determine the functional characteristics of AEB systems. As such, it was possible to implement an AEB function simulation based on actual vehicle test data.

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References

  • Bae, G., & Lee, S. (2020). A study on the simulation modeling method of LKAS test evaluation. Journal of the Korea Academia-Industrial Cooperation Society, 21(3), 57–64.

    Google Scholar 

  • Baek, S., et al. (2021). Development of AEBS simulation model for traffic accident analysis of vehicle with ADAS. Transaction of the Korean Society of Automotive Engineers, 29(11), 995–1001.

    Article  Google Scholar 

  • Bours, R. et al. (2013). A method for developing AEB systems based on integration of virtual and experimental tools. In 23rd International Technical Conference on the Enhanced Safety of Vehicles(ESV), pp. 13–0347.

  • Cho, H., et al. (2014). Usability analysis of collision avoidance system in vehicle-to-vehicle communication environment. Journal of Applied Mathematics, 2014, 951214.

    Article  Google Scholar 

  • Cho, D., et al. (2018). Interactive ADAS development and verification framework based on 3D car simulator. Journal of Institute of Korean Electrical and Electronics Engineers, 22(4), 970–977.

    Google Scholar 

  • Choi, Y., et al. (2017). A study on the applicability of AEBS according to radar angle using pc-crash and traffic accident database. Transaction of the Korean Society of Automotive Engineers, 25(6), 691–701.

    Article  Google Scholar 

  • Cicchino, J. B. (2017). Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates. Accident Analysis and Prevention, 99, 142–152.

    Article  Google Scholar 

  • EuroNCAP AEB Test Protocol, (2021). Test protocol—AEB car-to-car systems.

  • Fildes, B., et al. (2015). Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes. Accident Analysis & Prevention, 81, 24–29.

    Article  Google Scholar 

  • Han, J., et al. (2016). Vehicle distance estimation using a mono-camera for FCW/AEB systems. International Journal of Automotive Technology, 17(3), 493.

    Article  Google Scholar 

  • HMG., Maintanace Manual.

  • Hwang, J. (2020). Trend of sensor technology for autonomous driving. Auto Journal, pp. 18–21

  • Jang, H., et al. (2013). The safety evaluation method of advanced emergency braking system. Transaction of the Korean Society of Automotive Engineers, 21(5), 162–168.

    Article  Google Scholar 

  • Kapse, R., (2019). Implementing and autonomous emergency braking with simulink using two radar sensors. arXiv preprint, arXiv:1902.11210.

  • Kim, B., & Lee, S. (2020). Comparison of simulation and actual test for ACC function on real-road. Journal of the Korea Academia-Industrial Cooperation Society, 21(1), 457–467.

    Google Scholar 

  • Lee, J., et al. (2019). A study on the accident prevention effect of autonomous emergency braking system via meta-analysis. Transaction of the Korean Society of Automotive Engineers, 27(10), 811–818.

    Article  Google Scholar 

  • Lee, J., et al. (2019). Study on the improvement of a collision avoidance system for curves. Applied Science, 9(24), 5380.

    Article  Google Scholar 

  • Llic, V. et al. (2018). Development of sensor fusion based ADAS modules in virtual environments. In 2018 Zooming Innovation in Consumer Technologies Conference(ZINC), pp. 88–91.

  • Ortega, J., et al. (2020). Overtaking maneuver scenario building for autonomous vehicles with Prescan Software. Transportation Engineering, 2, 100029.

    Article  Google Scholar 

  • Son, T. D. et al. (2018). A simulation-based testing and validation framework for ADAS development. In Proceedings of 7th Transport Research Arena TRA 2018, April 16–19.

  • SAE Standard J3016, (2018). SAE on-road automated vehicle standards committee, taxonomy and definitions for terms related to on-rad motor vehicle automated driving systems.

  • Stanislas, L., & Peynot, T. (2015). Characterization of the delphi electronically scanning radar for robotics applications. In Proceedings of the Australian Conference on Robotics and Automation 2015. Australian Robotics and Automation Association, Australia, pp. 1–10.

  • Teoh, E. R. (2021). Effectiveness of front crash prevention systems in reducing large truck real-world crash rates. Traffic Injury Prevention, 22(4), 284–289.

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Forensic Service (NFS2024TAA01), Ministry of the Interior and Safety, Republic of Korea.

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Authors and Affiliations

  1. Traffic Accident Analysis Division, National Forensic Service, Wonju, 26460, Korea

    Jong Hyuk Kim, Jae Hyeong Lee, Ji Hun Choi & Woo Jeong Jeon

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  1. Jong Hyuk Kim
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  2. Jae Hyeong Lee
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  3. Ji Hun Choi
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  4. Woo Jeong Jeon
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Correspondence to Jong Hyuk Kim.

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Kim, J.H., Lee, J.H., Choi, J.H. et al. AEB Simulation Method Based on Vehicle Test Data for Accident Analysis of ADAS Vehicles. Int.J Automot. Technol. 25, 261–277 (2024). https://doi.org/10.1007/s12239-024-00019-5

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  • DOI: https://doi.org/10.1007/s12239-024-00019-5

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