title: Population distributions of time to collision at brake application during car following from naturalistic driving data.
abstract: OBJECTIVE
  Forward collision warning (FCW) systems are designed to mitigate the effects of rear-end collisions. Driver acceptance of these systems is crucial to their success, as perceived "nuisance" alarms may cause drivers to disable the systems. In order to make customizable FCW thresholds, system designers need to quantify the variation in braking behavior in the driving population. The objective of this study was to quantify the time to collision (TTC) that drivers applied the brakes during car following scenarios from a large scale naturalistic driving study (NDS).
  
  
  METHODS
  Because of the large amount of data generated by NDS, an automated algorithm was developed to identify lead vehicles using radar data recorded as part of the study. Using the search algorithm, all trips from 64 drivers from the 100-Car NDS were analyzed. A comparison of the algorithm to 7135 brake applications where the presence of a lead vehicle was manually identified found that the algorithm agreed with the human review 90.6% of the time.
  
  
  RESULTS
  This study examined 72,123 trips that resulted in 2.6 million brake applications. Population distributions of the minimum, 1st, and 10th percentiles were computed for each driver in speed ranges between 3 and 60 mph in 10 mph increments. As speed increased, so did the minimum TTC experience by drivers as well as variance in TTC. Younger drivers (18-30) had lower TTC at brake application compared to older drivers (30-51+), especially at speeds between 40 mph and 60 mph.
  
  
  CONCLUSIONS
  This is one of the first studies to use large scale NDS data to quantify braking behavior during car following. The results of this study can be used to design and evaluate FCW systems and calibrate traffic simulation models.
authors.affiliation: Virginia Tech, USA. Electronic address: kusano@vt.edu.
authors.forename: Kristofer D
authors.initials: KD
authors.lastname: Kusano
authors.affiliation: Virginia Tech, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.affiliation: Virginia Tech, USA.
authors.forename: Jade
authors.initials: J
authors.lastname: Montgomery
authors.affiliation: Virginia Tech, USA.
authors.forename: Hampton C
authors.initials: HC
authors.lastname: Gabler
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001334:Automobile Driving; D003710:Demography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011482:Protective Devices; D011930:Reaction Time; D013647:Task Performance and Analysis; D055815:Young Adult
keywords: Driver behavior; Forward collision warning; Naturalistic driving study; Time to collision