Assessing Driver Distraction: Enhancements of the ISO 26022 Lane Change Task to Make its Difficulty Adjustable

2023-01-0791

04/11/2023

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes.
In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject.
A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code. Therefore, a combination of vJoy, FreePIE, and Python scripts was used to add alterable levels of noise to the steering and accelerator pedal signals, simulating variable crosswinds and headwinds/tailwinds, thereby allowing task difficulty to be adjustable. These modifications made the LCT software much more useful for human factors research and evaluations.
In addition, to support future applications, baseline mean lateral deviation data from the intended path from several example studies are provided as well as data on the effects of age and various vehicle-related (e.g., navigation system use) and abstract reference tasks (e.g., Sternberg memory task).
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DOI
https://doi.org/10.4271/2023-01-0791
Pages
14
Citation
Zheng, H., Hu, F., and Green, P., "Assessing Driver Distraction: Enhancements of the ISO 26022 Lane Change Task to Make its Difficulty Adjustable," SAE Technical Paper 2023-01-0791, 2023, https://doi.org/10.4271/2023-01-0791.
Additional Details
Publisher
Published
Apr 11, 2023
Product Code
2023-01-0791
Content Type
Technical Paper
Language
English