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Target modeling and deduction of automotive radar resolution requirements for pedestrian classification

Published online by Cambridge University Press:  16 April 2015

Eugen Schubert ,
Martin Kunert ,
Frank Meinl  and
Wolfgang Menzel
Eugen Schubert*
Affiliation:
Robert Bosch GmbH, Chassis Systems Control, Advanced Engineering Sensor Systems, P.O. Box 16 61, 71226 Leonberg, Germany
Martin Kunert
Affiliation:
Robert Bosch GmbH, Chassis Systems Control, Advanced Engineering Sensor Systems, P.O. Box 16 61, 71226 Leonberg, Germany
Frank Meinl
Affiliation:
Robert Bosch GmbH, Chassis Systems Control, Advanced Engineering Sensor Systems, P.O. Box 16 61, 71226 Leonberg, Germany
Wolfgang Menzel
Affiliation:
Ulm University, Institute of Microwave Techniques, Ulm, Germany
*
Corresponding author: E. Schubert, Email: eugen.schubert@de.bosch.com
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Abstract

Pedestrian Collision Mitigation Systems (PCMS) are already in the market for some years. Due to continuously evolving EuroNCAP regulations their presence will increase. Visual sensors, already capable of pedestrian classification, provide functional benefits, because the reaction behavior can be optimized when the imminent collision object is recognized as pedestrian or cyclist. Nevertheless their performance will suffer under adverse environmental conditions like darkness, fog, rain or backlight. Even in such unfavorable situations the performance of radar sensors is not significantly deteriorated. Enabling classification capability to automotive radar will further improve road safety and will lower PCMS's overall costs. In this paper, a multi-reflection-point pedestrian target model based on motion analysis is presented. Together with an appropriate sensor model, pedestrian radar signal responses can be provided for a wide range of accident scenarios. Additionally velocity separation requirements that are needed for classification of pedestrians are derived from the simulations. Besides determination of classification features, the model discloses the limits of classical radar signal processing and further offers the opportunity to evaluate parametric spectral analysis. Based on simulated and measured baseband radar signals of pedestrians one of these techniques is deeper analyzed and its enhancement especially on the velocity separation capability is evaluated.

Keywords

Radar applicationsRadar signal processing and system modeling
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 7 , Special Issue 3-4: European Microwave Week 2014 , June 2015 , pp. 433 - 441
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2015 

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References

REFERENCES

[1] Bräuchle, C.; Flehmig, F.; Rosenstiel, W.; Kropf, T.: Maneuver decision for active pedestrian protection under uncertainty, in IEEE Annual Conf. on Intelligent Transportation Systems, Netherlands, 2013, 646–651.Google Scholar
[2] Rohling, H.; Heuel, S.; Ritter, H.: Pedestrian detection procedure integrated into an 24 GHz automotive radar, in IEEE Int. Radar Conf., Washington DC, USA, May 2010, 1229–1232.Google Scholar
[3] Fölster, F.; Rohling, H.; Ritter, H.: Observation of a walking pedestrian with a 24 GHz automotive radar sensor, in German Microwave Conf., Karlsruhe, Germany, 2006.Google Scholar
[4] Dorp, P.v.; Groen, F.C.A.: Human walking estimation with radar. IEE Proc. Radar, Sonar Navig., 150 (5) (2003), 356–365.Google Scholar
[5] Ghaleb, A.; Vignaud, L.; Nicolas, J.M.: Micro-Doppler analysis of wheels and pedestrians in ISAR imaging. IET Signal Process., 2 (3) (2008), 301–311.CrossRefGoogle Scholar
[6] Motion Capture Database of Carnegie Mellon University Graphics Lab, Pittsburgh, PA 15213, http://mocap.cs.cmu.edu Google Scholar
[7] Andres, M.; Menzel, W.; Bloecher, H.-L.; Dickmann, J.: Detection of slow moving targets using automotive radar sensors, in German Microwave Conf., Ilmenau, Germany, 2012, 1–4.Google Scholar
[8] Schubert, E.; Kunert, M.; Menzel, W.; Fortuny-Guasch, J.; Chareau, J.-M.: Human RCS measurements and dummy requirements for the assessment of radar based active pedestrian safety systems, in 14th Int. Radar Symp., Dresden, Germany, 2013, 752–757.Google Scholar
[9] Livingston, E.H.; Lee, S.: Body surface area prediction in normal-weight and obese patients. Am. J. Physiol. Endocrinol. Metab., 281 (2001), E586–E591.CrossRefGoogle ScholarPubMed
[10] Wisch, M.; Seiniger, P.; Pastor, C.; Edwards, M.; Visvikis, C.; Reeves, C.: Scenarios and weighting factors for pre-crash assessment of integrated pedestrian safety systems, Deliverable 1.1 of the public funded project ASPECSS (EU FP7), chapter 5, 59, 2013, http://www.aspecss-project.eu Google Scholar
[11] Kauppinen, I.; Roth, K.: Audio signal extrapolation – theory and applications, in Proc. of the 5th Int. Conf. on Digital Audio Effects, Hamburg, Germany, 2002, 105–110.Google Scholar
[12] Marple, L.: Resolution of conventional Fourier, autoregressive, and special ARMA methods of spectrum analysis, in IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 1977, 74–77.Google Scholar