Investigation of the Radar Cross-Section and its Optimization Potential for ADAS Tests
Received: 17 October 2024 | Revised: 5 November 2024 | Accepted: 9 January 2025 | Online: 2 February 2025
Corresponding author: Robert Magai
Abstract
The objective of this study is to examine the Radar Cross Section (RCS) of instruments designed for Autonomous Driving Systems (ADAS) testing, with the intention of comparing the results to those of actual human subjects. The RCS values of both dummy and platform objects were documented at varying distances and positions, with the objective of ascertaining the extent to which dummies can serve as substitutes for human values in vehicle radar sensing tests. The findings, substantiated by graphical representations and statistical analyses (e.g., Pearson and Spearman correlation), reveal a moderately strong positive correlation between the RCS and human values, which is statistically significant. The outcomes of the tests demonstrate that the developed instruments can substitute for real human radar cross-section values within the range of 5-15 m. However, as the distance increases, larger deviations are observed. These discrepancies underscore the necessity for a refinement of the dummy design in future ADAS tests, ensuring that distance-sensitive tests accurately reflect real human measurements.
Keywords:
dummy, RCS, autonomous driving systems, statistical analysisDownloads
References
F. E. Garcia-Muiña, R. González-Sánchez, A. M. Ferrari, and D. Settembre-Blundo, "The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company," Social Sciences, vol. 7, no. 12, Dec. 2018, Art. no. 255.
T. Zheng, M. Ardolino, A. Bacchetti, and M. Perona, "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," International Journal of Production Research, vol. 59, no. 6, pp. 1922–1954, Mar. 2021.
H. Fatorachian and H. Kazemi, "Impact of Industry 4.0 on supply chain performance," Production Planning & Control, vol. 32, no. 1, pp. 63–81, Jan. 2021.
M. A. Gerber, R. Schroeter, and B. Ho, "A human factors perspective on how to keep SAE Level 3 conditional automated driving safe," Transportation Research Interdisciplinary Perspectives, vol. 22, Nov. 2023, Art. no. 100959.
T. Ataman, M. A. Biberci, and M. B. Celik, "Simulation of Advanced Driving Assistance Systems for a Dynamic Vehicle Model," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16553–16558, Oct. 2024.
V. Saini, A. Jain, P. Shah, R. Sekhar, and P. Warrior, "Automated Driving Capabilities using advanced software tools to improve optimization for Autonomous Vehicle in Industry 4.0," in 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), Tiruchengode, India, Oct. 2023, pp. 1–6.
H. Estl, "Paving the way to self-driving cars with advanced driver assistance systems," Texas Instruments, Jul. 2020
J. P. Thalen, "ADAS for the Car of the Future," B. Thesis, Faculty of Engineering Technology, Industrial Design, University of Twente, Enschede, Netherlands, 2006.
A. Sheykhfard, F. Haghighi, E. Papadimitriou, and P. Van Gelder, "Review and assessment of different perspectives of vehicle-pedestrian conflicts and crashes: Passive and active analysis approaches," Journal of Traffic and Transportation Engineering (English Edition), vol. 8, no. 5, pp. 681–702, Oct. 2021.
M. E. A. Kanona, M. G. Hamza, A. G. Abdalla, and M. K. Hassan, "A Review of Ground Target Detection and Classification Techniques in Forward Scattering Radars," Engineering, Technology & Applied Science Research, vol. 8, no. 3, pp. 3018–3022, Jun. 2018.
S. Bertoldo, C. Lucianaz, and M. Allegretti, "On the Use of a 77 GHz Automotive Radar as a Microwave Rain Gauge," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2356–2360, Feb. 2018.
K. J. Anstey, J. Wood, S. Lord, and J. G. Walker, "Cognitive, sensory and physical factors enabling driving safety in older adults," Clinical Psychology Review, vol. 25, no. 1, pp. 45–65, Jan. 2005.
S. R. Flaxman et al., "Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis," The Lancet Global Health, vol. 5, no. 12, pp. e1221–e1234, Dec. 2017.
L. Hickson, J. Wood, A. Chaparro, P. Lacherez, and R. Marszalek, "Hearing Impairment Affects Older People’s Ability to Drive in the Presence of Distracters," Journal of the American Geriatrics Society, vol. 58, no. 6, pp. 1097–1103, 2010.
K. J. Anstey, M. S. Horswill, J. M. Wood, and C. Hatherly, "The role of cognitive and visual abilities as predictors in the Multifactorial Model of Driving Safety,"Accident; Analysis and Prevention, vol. 45, pp. 766–774, Mar. 2012.
J. Oxley, J. Charlton, J. Scully, and S. Koppel, "Older female drivers: An emerging transport safety and mobility issue in Australia," Accident Analysis & Prevention, vol. 42, no. 2, pp. 515–522, Mar. 2010.
P. M. Greenwood, J. K. Lenneman, and C. L. Baldwin, "Advanced driver assistance systems (ADAS): Demographics, preferred sources of information, and accuracy of ADAS knowledge," Transportation Research Part F: Traffic Psychology and Behaviour, vol. 86, pp. 131–150, Apr. 2022.
L. Li, D. Wen, N.-N. Zheng, and L.-C. Shen, "Cognitive Cars: A New Frontier for ADAS Research," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 395–407, Mar. 2012.
K. M. Tamás, "Luneberg-reflektor radarkeresztmetszetének mérése összehasonlító FDR módszerrel, " Hadmernok, vol. 2, no. 3, pp. 100–197, Sep. 2007.
Z. Liu, W. Ma, W. Liu, and S. Zhang, "Research on Calibration Technology of AEB Target Pedestrian Dummy Based on Radar and Infrared Reflected Signal," in 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Beihai, China, Jan. 2021, pp. 295–300.
G. J. Fortuny and J.-M. Chareau, Radar Cross Section Measurements of Pedestrian Dummies and Humans in the 24/77 GHz Frequency Bands. Luxembourg, Luxembourg: Publications office of the European Union, 2013.
D. Lv, L. Yuan, and X. Bai, "Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test," Scientific Reports, vol. 14, no. 1, Jan. 2024, Art. no. 2303.
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Copyright (c) 2025 Robert Magai, Balazs Molnar, Norbert Simon, Leticia Pekk
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