Advertisement

Using Naturalistic Driving Research to Design, Test and Evaluate Driver Assistance Systems

  • Gregory M. Fitch
  • Richard J. Hanowski

Abstract

Naturalistic driving research is the in situ investigation of driver performance and behavior. Video cameras and a suite of sensors are installed on participants own vehicles and are used to continuously record the driver, the vehicle, and the environment over an extended period of time. The collected data typically span hundreds of thousands of vehicle-miles-traveled and provide an “instant replay” of the rare occurrence of safety-critical events. The method supports the representative design of experiments, where the drivers, vehicles, and environment sampled are representative of the conditions to which the results are applied. Naturalistic Driving Studies (NDS) are an effective tool for the design, testing, and evaluation of driver assistance systems. This is because they can support various stages of a user-center systems design process. First, NDSs can help determine what drivers need from a new driver assistance system by allowing researchers to assess the driver error contributing to safety-critical events. Secondly, the approach can serve the testing of working prototypes, where “natural” driver behavior and performance with the candidate driver assistance systems is observed. Thirdly, novel test criteria, such as drivers’ rate of involvement in safety-critical events, can be used to evaluate the driver assistance systems’ effectiveness at improving driver performance. NDSs and their role in the design, testing, and evaluation of driver assistance systems are described in this chapter.

Keywords

Driver Behavior Lane Change Driver Assistance System Driver Performance Light Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Barfield W, Dingus TA (1998) Human factors in intelligent transporation systems. Lawrence Earlbaum, MahwahGoogle Scholar
  2. Blanco M, Bocanegra JL, Morgan JF, Fitch GM, Medina A, Olson RL, Hanowski RJ, Daily B, Zimmermann RP, Howarth HD, Di Domenico TE, Barr LC, Popkin SM, Green K (2009) Assessment of a drowsy driver warning system for heavy-vehicle drivers: final report. NHTSA Contract No. DTNH22-05-D-01019, Task Order #18. National Highway Traffic Administration, Washington, DCGoogle Scholar
  3. Blanco M, Hickman JS, Olson RL, Bocanegra JL, Hanowski RJ, Nakata A, Greening M, Madison P, Holbrook GT, Bowman D (in press) Investigating critical incidents, driver restart period, sleep quantity, and crash countermeasures in commercial operations using naturalistic data collection. Contract No. DTFH61-01-C-00049, Task Order # 23. Federal Motor Carrier Safety Administration, USDOT, Washington, DCGoogle Scholar
  4. Chapanis A (1996) Human factors in systems engineering. Wiley, New YorkGoogle Scholar
  5. Department of Defense Systems Management College (2001) Systems engineering fundamentals. Defense Acquisition University Press, Fort BelvoirGoogle Scholar
  6. Dingus TA (2003) Human factors applications in surface transportation. Front Eng 8:39–42Google Scholar
  7. Dingus TA (2008) Naturalistic driving: need, history and some early results. Retrieved 8 Nov 2010 from http://www.vtti.vt.edu/PDF/ndmas_ppt_PDFs/dingusVTTI.pdf. Accessed 15 June 2011
  8. Dingus T, Klauer S, Neale VL, Petersen A, Lee SE, Sudweeks J, Perez M, Hankey J, Ramsey D, Gupta S, Busher C, Doerzaph Z, Jermeland J, Knipling R (2006) The 100-car naturalistic driving study, Phase II – results of the 100-car field experiment. National Highway Safety Administration (NHTSA), Washington, DCGoogle Scholar
  9. Fitch GM, Lee SE, Klauer S, Hankey JM, Sudweeks J, Dingus TA (2009) Analysis of lane-change crashes and near-crashes. Technical Report No. DTNH22-00-C-07007, Task Order 23. National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  10. Fitch GM, Blanco M, Camden M, Olson R, McClafferty J, Morgan JF, Wharton AE, Howard H, Trimble T, Hanowski RJ (in press) Field demonstration of heavy vehicle camera/video imaging systems: final report. Contract No. DTNH22-05-D-01019, Task Order #23. National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  11. Fitch GM, Blanco M, Camden MC, Hanowski RJ (2011a) Field demonstration of a camera/video imaging system for heavy vehicles. In: Proceedings of the society of automotive engineers commercial vehicle engineering congress and exhibition, Chicago, ILGoogle Scholar
  12. Fitch GM, Schaudt WA, Wierwille WW, Blanco M, Hanowski RJ (2011b) Human factors and systems engineering of a camera/video imaging system. In: Proceedings of the 18th World congress on intelligent transportation systems, Washington, DCGoogle Scholar
  13. General Motors Corporation (2005) Automotive collision avoidance system field operational test (ACAS FOT) final program report. Technical Report No. DOT HS 809 886. General Motors Corporation and National Highway Traffic Safety Administration, WarrenGoogle Scholar
  14. Hammond KR, Stewart TR (2001) The essential Brunswik: beginnings, explications, applications. Oxford University Press, Oxford/New YorkGoogle Scholar
  15. Hanowski RJ, Blanco M, Nakata A, Hickman JS, Schaudt WA, Fumero MC, Olson R, Jermeland J, Greening M, Holbrook GT, Knipling RR, Madison P (2008) The drowsy driver warning system field operational test: data collection methods final report No. DOT HS 810 035, Washington, DCGoogle Scholar
  16. Hickman JS, Hanowski RJ, Bocanegra J (2010) Distraction in commercial trucks and buses: assessing prevalence and risk in conjunction with crashes and near-crashes. Report No. FMCSA-RRR-10-049. Federal Motor Carrier Safety Administration, Washington, DCGoogle Scholar
  17. Hickman JS, Knipling RR, Olson RL, Fumero MC, Blanco M, Hanowski RJ (in press) Heavy vehicle-light vehicle interaction data collection and countermeasure research project, Phase 1 – preliminary analysis of data collected in the drowsy driver warning system field operational test: Task 5, preliminary analysis of drowsy driver warning system field operational test data. Contract No. DTNH22-00-C-07007, Task Order 21. Motor Carrier Safety Administration, Washington, DCGoogle Scholar
  18. Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey DJ (2006) The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data (No. DOT-HS-810-594). NHTSA, Washington, DCGoogle Scholar
  19. Klauer S, Holmes L, Harwood L, Doerzaph Z (in press) Toward development of design guidelines for connected vehicles systems: evaluation of display location and application type on driving performance. National Highway Traffic Safety Administration, Washington, DCGoogle Scholar
  20. LeBlanc D, Sayer J, Winkler C, Ervin R, Bogard S, Devonshire J, Mefford M, Hagan M, Bareket Z, Goodsell R, Gordon T (2006) Road departure crash warning system field operational test: methodology and results (No. UMTRI-2006-9-2). The University of Michigan Transportation Research Institute, Ann ArborGoogle Scholar
  21. McLaughlin SB, Hankey JM, Dingus TA (2008) A method for evaluating collision avoidance systems using naturalistic driving data. Accid Anal Prev 40(1):8–16CrossRefGoogle Scholar
  22. Olson RL, Hanowski RJ, Hickman JS, Bocanegra J (2009) Driver distraction in commercial vehicle operations: final report. Contract DTMC75-07-D-00006, Task Order 3. Federal Motor Carrier Safety Administration, Washington, DCGoogle Scholar
  23. Owens JM, McLaughlin SB, Sudweeks J (2010) On-road comparison of driving performance measures when using handheld and voice-control interfaces for mobile phones and portable music players. SAE Int J Passenger Cars Mech Syst 3(1):734–743Google Scholar
  24. Rivera AJ, Karsh B-T (2008) Human factors and systems engineering approach to patient safety for radiotherapy. Int J Radiat Oncol Biol Phys 71(Suppl 1):S174–S177CrossRefGoogle Scholar
  25. Sanders MS, McCormick EJ (1993) Human factors in engineering design, 6th edn. McGraw-Hill, New YorkGoogle Scholar
  26. Treat et al (1979) Tri-level study of the causes of traffic crashes: final report. Volume I: causal factor tabulations and assessments: institute for research in public safety, Indiana UniversityGoogle Scholar
  27. U.S. Department of Transportation (2009) National automotive sampling system general estimates system. Retrieved Nov 2010 from http://www.nhtsa.gov/people/ncsa/nass_ges.html
  28. Vicente K (1999) Cognitive work analysis: towards safe, productive, and healthy computer-based work, vol 1. Lawrence Erlbaum, MahwahGoogle Scholar
  29. Wierwille WW (1981) Statistical techniques for instrument panel arrangement. In: Proceedings of the NATA conference series III. Human Factors, New York, pp 201–218Google Scholar
  30. Wierwille WW, Schaudt WA, Fitch GM, Hanowski RJ (2007) Development of a performance specification for indirect visibility systems on heavy trucks. Paper Number 2007-01-4231. SAE Trans J Commer Vehicles 2(116):264–275Google Scholar
  31. Wierwille WW, Schaudt WA, Spaulding JM, Gupta SK, Fitch GM, Wiegand DM, Hanowski RJ (2008) Development of a performance specification for indirect visibility systems in heavy vehicles final report supporting research. Report No. DOT HS 810 960. U.S. Department of Transportation, National Highway Traffic Safety Administration,Washington, DCGoogle Scholar
  32. Wierwille WW, Schaudt WA, Blanco M, Alden A, Hanowski RJ (in press) Enhanced camera/video imaging systems (e-c/viss) for heavy vehicles: final report. Contract No. DTNH22-05-D-01019, Task Order 6 (Submitted Sept 2008). U.S. Department of Transportation, National Highway Traffic Safety Administration, Washington, DCGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.Virginia Tech Transportation Institute-Truck and Bus SafetyBlacksburgUSA
  2. 2.Center for Truck and Bus SafetyVirginia Tech Transportation Research InstituteBlacksburgUSA

Personalised recommendations