title: Effectiveness and acceptance of the intelligent speeding prediction system (ISPS).
abstract: BACKGROUND
  The intelligent speeding prediction system (ISPS) is an in-vehicle speed assistance system developed to provide quantitative predictions of speeding. Although the ISPS's prediction of speeding has been validated, whether the ISPS can regulate a driver's speed behavior or whether a driver accepts the ISPS needs further investigation. Additionally, compared to the existing intelligent speed adaptation (ISA) system, whether the ISPS performs better in terms of reducing excessive speeds and improving driving safety needs more direct evidence.
  
  
  OBJECTIVE
  An experiment was conducted to assess and compare the effectiveness and acceptance of the ISPS and the ISA.
  
  
  METHODS
  We conducted a driving simulator study with 40 participants. System type served as a between-subjects variable with four levels: no speed assistance system, pre-warning system developed based on the ISPS, post-warning system ISA, and combined pre-warning and ISA system. Speeding criterion served as a within-subjects variable with two levels: lower (posted speed limit plus 1 mph) and higher (posted speed limit plus 5 mph) speed threshold. Several aspects of the participants' driving speed, speeding measures, lead vehicle response, and subjective measures were collected.
  
  
  RESULTS
  Both pre-warning and combined systems led to greater minimum time-to-collision. The combined system resulted in slower driving speed, fewer speeding exceedances, shorter speeding duration, and smaller speeding magnitude.
  
  
  CONCLUSIONS
  The results indicate that both pre-warning and combined systems have the potential to improve driving safety and performance.
authors.affiliation: State University of New York at Buffalo, USA.
authors.forename: Guozhen
authors.initials: G
authors.lastname: Zhao
authors.forename: Changxu
authors.initials: C
authors.lastname: Wu
mesh_terms: D000054:Acceleration; D000063:Accidents, Traffic / Q000517:prevention & control*; D000328:Adult; D001185:Artificial Intelligence; D001334:Automobile Driving; D005260:Female; D006801:Humans; D008297:Male; D010342:Patient Acceptance of Health Care; D012309:Risk-Taking; D055815:Young Adult