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Comparison of Time to Collision and Enhanced Time to Collision at Brake Application during Normal Driving
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 05, 2016 by SAE International in United States
Annotation ability available
The effectiveness of Forward Collision Warning (FCW) or similar crash warning/mitigation systems is highly dependent on driver acceptance. If a FCW system delivers the warning too early, it may distract or annoy the driver and cause them to deactivate the system. In order to design a system activation threshold that more closely matches driver expectations, system designers must understand when drivers would normally apply the brake. One of the most widely used metrics to establish FCW threshold is Time to Collision (TTC). One limitation of TTC is that it assumes constant vehicle velocity. Enhanced Time to Collision (ETTC) is potentially a more accurate metric of perceived collision risk due to its consideration of vehicle acceleration. This paper compares and contrasts the distribution of ETTC and TTC at brake onset in normal car-following situations, and presents probability models of TTC and ETTC values at braking across a range of vehicle speeds. The data source of this study was the 100-Car Naturalistic Driving Study (NDS). The study is based on a total of 72,380 trips, resulting in over 870,000 braking events with a closing lead vehicle. The resultant models provide the probability of occurrence across a range of continuous ETTC and TTC. Compared to TTC, ETTC distributions were shown to have lower variance between drivers across all vehicle speed ranges. The current study is the first large scale naturalistic data analysis to characterize probability of brake application in normal driving, and will provide valuable data on driver braking behavior to improve future forward collision warning/mitigation systems.
CitationChen, R., Sherony, R., and Gabler, H., "Comparison of Time to Collision and Enhanced Time to Collision at Brake Application during Normal Driving," SAE Technical Paper 2016-01-1448, 2016, https://doi.org/10.4271/2016-01-1448.
- Mehler, B., Reimer, B., Lavallière, M., Dobres, J., and Coughlin, J.F., “Evaluating Technologies Relevant to the Enhancement of Driver Safety,” Washington, DC: AAA Foundation for Traffic Safety, 2014.
- Cicchino, J.B. and Mccartt, A.T., “Experiences of Dodge and Jeep Owners with Collision Avoidance and Related Technologies,” Arlington, VA: Insurance Institute for Highway Safety, 2014.
- Brunson, S.J., Kyle, E.M., Phamdo, N.C., and Preziotti, G.R., Alert Algorithm Development Program - NHTSA Rear-End Collision Alert Algorithm, (DOT HS 809 526), 2002.
- Montgomery, J., Kusano, K.D., and Gabler, H.C., “Age and Gender Differences in Time to Collision at Braking From the 100-Car Naturalistic Driving Study,” Traffic Inj. Prev. 15(sup1):S15-S20, 2014, doi:10.1080/15389588.2014.928703.
- Kusano, K.D., Chen, R., Montgomery, J., and Gabler, H.C., “Population distributions of time to collision at brake application during car following from naturalistic driving data,” J. Safety Res. 54:95.e29-104, 2015, doi:10.1016/j.jsr.2015.06.011.
- Lee, D.N., “A theory of visual control of braking based on information about time-to-collision.,” Perception 5(4):437-459, 1976, doi:10.1068/p050437.
- National Highway Traffic Safety Administration, “Forward Collision Warning System Confirmation Test,” U.S. Department of Transportation, Washington, DC, 2013.
- ISO 15623:2013 (E), “Intelligent Transport Systems -Forward Vehicle Collision Warning Systems - Performance Requirements and Test Procedures,” Second Edi, 2013.
- Dingus, T., Klauer, S., Neale, V., Petersen, A., Lee, S.E., Sudweeks, J., Perez, M., Hankey, J., Ramsey, D., Gupta, S., Bucher, C., Doerzaph, Z.., Jermeland, J., and Knipling, R.., “The 100-Car Naturalistic Driving Study, Phase II - Results of the 100-Car Field Experiment,” (DOT HS 810 593), 2006.
- National Highway Traffic Safety Administration, “Traffic Safety Facts 1999: A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System,” Washington, DC, 2000.
- Federal Highway Adminstration, “Distribution of licensed drivers - 2012 by sex and percentage in each age group and relation to population,” http://www.fhwa.dot.gov/policyinformation/statistics/2012/dl20.cfm, Apr. 2015.
- Neale, V.L., Klauer, S.G., Knipling, R.R., Dingus, T.A., Holbrook, G.T., and Petersen, A., The 100 Car Naturalistic Driving Study Phase I - Experimental Design, (DOT HS 808 536), 2002.
- Kusano, K.D., Montgomery, J., and Gabler, H.C., “Methodology for identifying car following events from naturalistic data,” IEEE Intell. Veh. Symp. 281-285, 2014, doi:10.1109/IVS.2014.6856406.
- Burnham, K.P., Anderson, D.R., and Huyvaert, K.P., “AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons,” Behav. Ecol. Sociobiol. 65(1):23-35, 2011, doi:10.1007/s00265-010-1029-6.
- Davis, T.M., Mörtsell, E., Sollerman, J., Becker, A.C., Blondin, S., Challis, P., Clocchiatti, A., Filippenko, A. V, Foley, R.J., Garnavich, P.M., Jha, S., Krisciunas, K., Kirshner, R.P., Leibundgut, B., Li, W., Matheson, T., Miknaitis, G., Pignata, G., Rest, A., Riess, A.G., Schmidt, B.P., Smith, R.C., Spyromilio, J., Stubbs, C.W., Suntzeff, N.B., Tonry, J.L., Wood-Vasey, W.M., and Zenteno, A., “Scrutinizing Exotic Cosmological Models Using ESSENCE Supernova Data Combined with Other Cosmological Probes,” Astrophys. J. 666(4):716, 2007.
- Liddle, A.R., “How many cosmological parameters?,” Mon. Not. R. Astron. Soc. 351(3):L49-L53, 2004, doi:10.1111/j.1365-2966.2004.08033.x.
- Akaike, H., “A new look at the statistical model identification,” IEEE Trans. Automat. Contr. 19(6), 1974, doi:10.1109/TAC.1974.1100705.
- Schwarz, G., “Estimating the Dimension of a Model,” Ann. Stat. 6(2):461-464, 1978, doi:10.1214/aos/1176344136.
- McClafferty, J. and Hankey, J., 100-Car Reanalysis : Summary of Primary and Secondary Driver Characteristics, Report Number 10-UT-007, 2010.
- McLaughlin, S.B., Hankey, J.M., Dingus, T.A., and Klauer, S.G., Development of an FCW algorithm evaluation methodology with evaluation of three alert algorithms, DOT HS 811, 2009.
- Fisher, R.A. and Tippett, L.H.C., “Limiting forms of the frequency distribution of the largest or smallest member of a sample,” Math. Proc. Cambridge Philos. Soc. 24:180-190, 1927, doi::10.1017/S0305004100015681.
- Jenkinson, A.F., “The Frequency Distribution of the Annual Maximum (or Minimum) of Meteorological Elements,” Q. J. R. Meterorological Soc. 81(348):158-171, 1955, doi:10.1002/qj.49708134804.
- Torrielli, A., Pia, M., and Solari, G., “Extreme wind speeds from long-term synthetic records,” Jnl. Wind Eng. Ind. Aerodyn. 115:22-38, 2013, doi:10.1016/j.jweia.2012.12.008.
- Fernandes, W. and Naghettini, M., “A Bayesian approach for estimating extreme flood probabilities with upper-bounded distribution functions,” 1127-1143, 2010, doi:10.1007/s00477-010-0365-4.
- Rocco, M., “Extreme Values in Finance: A Survey,” J. Econ. Surv. 28(1):82-108, 2014, doi:10.1111/j.1467-6419.2012.00744.x.
- Songchitruksa, P. and Tarko, A.P., “The extreme value theory approach to safety estimation,” Accid. Anal. Prev. 38:811-822, 2006, doi:10.1016/j.aap.2006.02.003.
- Zheng, J., Suzuki, K., and Fujita, M., “Predicting driver’s lane-changing decisions using a neural network model,” Simul. Model. Pract. Theory 42:73-83, 2014, doi:10.1016/j.simpat.2013.12.007.
- Transportation Research Board, “Implementing the Results of the Second Strategic Highway Research Program: Saving Lives, Reducing Congestion, Improving Quality of Life,” Washington, DC, ISBN 9780309126069, 2009.