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Encyclopedia of Sustainability Science and Technology pp 64–89Cite as

Active Pedestrian Protection System, Scenario-Driven Search Method for

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Definition of the Subject and Its Importance

This paper presents an application of a pedestrian detection system aimed at localizing potentially dangerous situations in specific urban scenarios. The approach used in this work differs from the ones implemented in traditional pedestrian detection systems, which are designed to localize all pedestrians in the area in front of the vehicle. Conversely this approach searches for pedestrians in critical areas only.

The great advantages of such an approach are that pedestrian recognition is performed on limited image areas – therefore boosting its time-wise performance – and no assessment on the danger level is finally required before providing the result to either the driver or an onboard computer for automatic maneuvers. A further advantage is the drastic reduction of false alarms, making this system robust enough to control nonreversible safety systems.

Introduction

This paper presents an innovative approach to the detection of pedestrians,...

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Abbreviations

ADAS:

Advanced Driver Assistance Systems are complex systems to help the driver during specific maneuvers, or during normal driving.

Laser scanner:

Laser scanner is an optical sensor based on LIDAR (Light Detection And Ranging) technology that measures the distance of targets measuring the time delay between transmission of a pulse and detection of the reflected signal.

NIR:

Near InfraRed (0.75–1.4 μm) is the wavelength closest to visible that is often used in image processing for night vision, together with specific illuminators.

Bibliography

  1. O’Farrell J (2006) May contain nuts. Black Swan, London

    Google Scholar 

  2. Lucchini E, Kramer M (1981) Safety device for the protection of pedestrians. Patent

    Google Scholar 

  3. Matsuura Y, Maki T (2001) Device for reducing the impact of pedestrians. Patent

    Google Scholar 

  4. Myrholt H, Ericsson M (2002) Deployable A-pillar covers for pedestrian protection. Patent

    Google Scholar 

  5. Buehler M, Iagnemma K, Singh S (eds) (2010) The DARPA urban challenge. Springer, Berlin

    Google Scholar 

  6. Agran PF, Winn DG, Anderson CL, Tran C, Del Valle CP (1996) The role of the physical and traffic environment in child pedestrian injuries. Pediatrics 1996(98):1096–1103

    Google Scholar 

  7. Retting RA, Ferguson SA, McCartt AT (2003) A review of evidence-based traffic engineering measures designed to reduce pedestrian-motor vehicle crashes. J Public Health 2003(93):1456–1463

    Google Scholar 

  8. Premebida C, Monteiro G, Nunes U, Peixoto P (2007) A lidar and vision-based approach for pedestrian and vehicle detection and tracking. In: Proceedings of IEEE international conference on intelligent transportation systems, Seattle, pp 1044–1049

    Google Scholar 

  9. Hwang JP, Cho SE, Ryu KJ, Park S, Kim E (2007) Multi-classifier based LIDAR and camera fusion. In: Proceedings of the IEEE international conference on intelligent transportation systems, Seattle, pp 467–472

    Google Scholar 

  10. Franke U, Heinrich S (2002) Fast obstacle detection for urban traffic situations. IEEE Trans Intell Transport Syst 3(3):173–181

    Article  Google Scholar 

  11. Curio C, Edelbrunner J, Kalinke T, Tzomakas C, Von Seelen W (2000) Walking pedestrian recognition. IEEE Trans Intell Transport Syst 1(3):155–163

    Article  Google Scholar 

  12. Gandhi T, Trivedi MM (2007) Pedestrian protection systems: issues, survey, and challenges. IEEE Trans Intell Transport Syst 8(3):413–430

    Article  Google Scholar 

  13. Hodzic M (2008) Distronic plus and brake assist plus reduce rear-end collisions by 20%. http://www.benzinsider.com/2008/06/distronic-plus-and-brake-assist-plus-reduce-rear-end-collisions-by-20/. Accessed 30 May 2010

  14. The Motor report (2010) Toyota Prius To ship with advanced safety gear. http://www.themotorreport.com.au/29611/2010-toyota-prius-to-ship-with-advanced-safety-gear. Accessed 30 May 2010

  15. Juliussen E (2010) Driver assist systems. http://www.autofocusasia.com/electrical_electronics/driver_assist_systems.htm. Accessed 30 May 2010

  16. http://www.prevent-ip.org/en/prevent_subprojects/intersection_safety/intersafe/

  17. BBC News (2010) Self-parking car hits the shops. http://news.bbc.co.uk/2/hi/technology/3198619.stm. Accessed 31 May 2010

  18. www.trafficcalming.org

  19. Clarke A, Dornfeld M J (1994) Traffic calming, auto-restricted zones and other traffic management techniques: their effects on bicycling and pedestrians. national bicycling and walking study, Case Study No. 19, Federal Highway Administration 1994

    Google Scholar 

  20. Huang HF, Cynecki MJ (2001) The effects of traffic calming measures on pedestrian and motorist behavior. Research report FHWA-RD-00-104, Federal Highway Administration, U.S. Department of Transportation

    Google Scholar 

  21. Crandall JR, Bhalla KS, Madeley NJ (2002) Designing road vehicles for pedestrian protection. Brit Med J 324(7346):1145–1148

    Article  CAS  Google Scholar 

  22. Yao J, Yang J, Otte D (2008) Investigation of head injuries by reconstructions of real-world vehicle-versus-adult-pedestrian accidents. Saf Sci 46(7):1103–1114

    Article  Google Scholar 

  23. Koch W, Howard M (2003) Comprehensive approach to increased pedestrian safety in pedestrian – Car accidents. Proc Inst Mech Eng Part D J Automobile Eng 217:513–519

    Article  Google Scholar 

  24. Fröming R, Schindler V, Kühn M (2006) Requirement engineering for active safety pedestrian protection systems based on accident research. In: Advanced microsystems for automotive applications, New York, pp 79–106

    Google Scholar 

  25. Schuster PJ (2006) Current trends in bumper design for pedestrian impact. SAE Paper No.: 2006-01-0464

    Book  Google Scholar 

  26. Fredriksson R, Håland Y, Yang J (2010) Evaluation of a new pedestrian head injury protection system with a sensor in the bumper and lifting of the bonnet’s rear part. http://www.autoliv.com/wps/wcm/connect/5f997a004ce4f3f8ac68eef594aebdee/Pedestrian.pdf?MOD=AJPERES. Accessed 30 May 2010

  27. http://www.ivss.se/

  28. Broge JL (2010) Autoliv actively saving lives. http://www.sae.org/automag/techbriefs/11-2001/page2.htm. Accessed 30 May 2010

  29. Autoliv (2010) Pedestrian protection. http://www.autoliv.com/wps/wcm/connect/autoliv/Home/What+We+Do/Recent%20Innovations/Pedestrian%20Protection. Accessed 30 May 2010

  30. Kerkeling C, Schäfer J (2005) Structural hood and hinge concepts for pedestrian protection. In: Proceedings of 19th international technical conference on enhanced safety, Washington, DC, pp 379–389

    Google Scholar 

  31. Gernóimo D, López AM, Sappa AD, Graf T (2010) Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans Pattern Anal Mach Intell 32(7):1239–1258

    Article  Google Scholar 

  32. Sun Z, Bebis G, Miller R (2006) On-road vehicle detection: a review. IEEE Trans Pattern Anal Mach Intell 28(5):1–18

    Article  Google Scholar 

  33. Hussein M, Porikli F, Davis L (2009) A comprehensive evaluation framework and a comparative study of human detector. IEEE Trans Intell Transport Syst 10(3):417–427

    Article  Google Scholar 

  34. Enzweileer M, Gavrila DM (2009) Monocular pedestrian detection: survey and experiments. IEEE Trans Pattern Anal Mach Intell 31(2):2179–2195

    Article  Google Scholar 

  35. Folster F, Rohling H, Meinecke M-M (2005) Pedestrian recognition based on automotive radar sensors. In: 5th European congress and exhibition on ITS and services, Hanover

    Google Scholar 

  36. Ritter H, Rohling H (2007) Pedestrian detection based on automotive radar. In: Proceedings of the international conference on radar systems, Edinburgh

    Google Scholar 

  37. David K, Flach A (2010) CAR-2-X and pedestrian safety. IEEE Veh Technol Mag 3:70–76

    Article  Google Scholar 

  38. http://www.watchover-eu.org/

  39. http://www.project-amulette.de/

  40. Broggi A, Cerri P, Ghidoni S, Grisleri P, Jung HG (2008) Localization and analysis of critical areas in urban scenarios. In: Proceedings of IEEE intelligent vehicles symposium, Eindhoven, pp 1074–1079

    Google Scholar 

  41. Leyrit L, Chateau C, Tournayre C, Lapresté J-T (2008) Association of Adaboost and kernel based machine learning methods for visual pedestrian recognition. In: Proceedings of IEEE intelligent vehicles symposium, Eindhoven, pp 67–72

    Google Scholar 

  42. Geismann P, Schneider G (2008) A two-staged approach to vision- based pedestrian recognition using Haar and HOG features. In: Proceedings of IEEE intelligent vehicles symposium, Eindhoven, pp 554–559

    Google Scholar 

  43. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the international conference on computer vision and pattern recognition, vol 1. Hawaii, pp 511–518

    Google Scholar 

  44. Fuerstenberg K, Schulz R (2006) Laserscanners for driver assistance. In: Proceedings of the international workshop on intelligent transportation, Hamburg, pp 155–159

    Google Scholar 

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Author information

Authors and Affiliations

  1. VisLab – Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Parma, Parma, Italy

    Dr. Alberto Broggi, Dr. Pietro Cerri & Dr. Paolo Grisleri

  2. Intelligent Autonomous System Laboratory (IAS-Lab), Department of Information Engineering, University of Padua, Padova, Italy

    Dr. Stefano Ghidoni

  3. The School of Mechanical Engineering, Hanyang University, 222 Wangsimni-ro, Seondong-gu, Seoul, 133-791, South Korea

    Dr. Ho Gi Jung

Authors
  1. Dr. Alberto Broggi
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  2. Dr. Pietro Cerri
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  3. Dr. Stefano Ghidoni
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  4. Dr. Paolo Grisleri
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  5. Dr. Ho Gi Jung
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Corresponding author

Correspondence to Alberto Broggi .

Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, Larkspur, CA, USA

    Robert A. Meyers

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© 2012 Springer Science+Business Media, LLC

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Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P., Jung, H.G. (2012). Active Pedestrian Protection System, Scenario-Driven Search Method for. In: Meyers, R.A. (eds) Encyclopedia of Sustainability Science and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_486

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