Literature DB >> 26924947

Gap Acceptance During Lane Changes by Large-Truck Drivers-An Image-Based Analysis.

Kazutoshi Nobukawa1, Shan Bao1, David J LeBlanc1, Ding Zhao2, Huei Peng2, Christopher S Pan3.   

Abstract

This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

Entities:  

Keywords:  Active safety; gap analysis; lane change; large truck safety; naturalistic driving data

Year:  2015        PMID: 26924947      PMCID: PMC4766594          DOI: 10.1109/TITS.2015.2482821

Source DB:  PubMed          Journal:  IEEE trans Intell Transp Syst        ISSN: 1524-9050            Impact factor:   6.492


  2 in total

1.  A theory of visual control of braking based on information about time-to-collision.

Authors:  D N Lee
Journal:  Perception       Date:  1976       Impact factor: 1.490

2.  Visual control of braking: a test of the tau hypothesis.

Authors:  E H Yilmaz; W H Warren
Journal:  J Exp Psychol Hum Percept Perform       Date:  1995-10       Impact factor: 3.332

  2 in total
  2 in total

1.  Detection of Lane-Change Events in Naturalistic Driving Videos.

Authors:  Shuhang Wang; Brian R Ott; Gang Luo
Journal:  Intern J Pattern Recognit Artif Intell       Date:  2018-10       Impact factor: 1.373

2.  Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic.

Authors:  Chang Wang; Qinyu Sun; Zhen Li; Hongjia Zhang
Journal:  Sensors (Basel)       Date:  2020-04-16       Impact factor: 3.576

  2 in total

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