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Journal Article

Citation

Dinakar S, Muttart JW, Edewaard DE, Giannone M, Dickson C. Transp. Res. Rec. 2022; 2676(2): 706-717.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211045368

PMID

unavailable

Abstract

A cut-in or cut-off scenario involves a vehicle intruding into the path of another vehicle traveling in the same direction. These lane changes can lead to potentially dangerous situations, either a sideswipe or a rear-end crash. In this study, 552 cut-in events were analyzed, including four crash and 548 near-crash events from the Second Strategic Highway Research Program (SHRP-2) data set. Video and onboard-data-recorder data from the responding vehicle were used to analyze various factors associated with drivers' responses. Driver response times were measured from three different event onsets, and the effects of different factors on the respective response times were measured. These factors included the behavior of the subject driver, the behavior of the intruding vehicle/principal other vehicle (POV), and different environmental and infrastructural factors. The results showed that drivers responded more slowly when the POV took longer to move laterally to the subject driver's lane edge and faster when this time was short. Similarly, drivers responded faster to merging vehicles that started from a stop. Yet, response times were no different when the POV utilized a directional signal. These results point to a kinematic threshold involving lateral distance and lateral speed that best describes how drivers were triggered to respond. Drivers also responded faster near intersections, and at night. The results can be utilized to design crash mitigation systems in autonomous vehicles, as well as non-automated vehicles, to supplement human responses where their abilities may be lacking.


Language: en

Keywords

bicycles; driver behavior; hazard perception; human factors; human factors of vehicles; naturalistic data studies; pedestrians

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