scholarly journals An observational study of secondary task engagement while driving on urban streets in Iranian Safe Communities

2016 ◽  
Vol 96 ◽  
pp. 56-63 ◽  
Author(s):  
Javad Torkamannejad Sabzevari ◽  
Amir Reza Nabipour ◽  
Narges Khanjani ◽  
Ali Molaei Tajkooh ◽  
Mark J.M. Sullman
2019 ◽  
Vol 120 ◽  
pp. 290-298
Author(s):  
Anja Katharina Huemer ◽  
Selvi Gercek ◽  
Mark Vollrath

2021 ◽  
Vol 151 ◽  
pp. 105959
Author(s):  
Alexandria M. Noble ◽  
Melissa Miles ◽  
Miguel A. Perez ◽  
Feng Guo ◽  
Sheila G. Klauer

2016 ◽  
Vol 93 ◽  
pp. 48-54 ◽  
Author(s):  
Fearghal O’Brien ◽  
Sheila G. Klauer ◽  
Johnathon Ehsani ◽  
Bruce G. Simons-Morton

2019 ◽  
Vol 11 (3) ◽  
pp. 59-70
Author(s):  
Dina Kanaan ◽  
Suzan Ayas ◽  
Birsen Donmez ◽  
Martina Risteska ◽  
Joyita Chakraborty

This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.


Author(s):  
Dengbo He ◽  
Birsen Donmez

State-of-the-art vehicle automation requires drivers to visually monitor the driving environment and the automation (through interfaces and vehicle’s actions) and intervene when necessary. However, as evidenced by recent automated vehicle crashes and laboratory studies, drivers are not always able to step in when the automation fails. Research points to the increase in distraction or secondary-task engagement in the presence of automation as a potential reason. However, previous research on secondary-task engagement in automated vehicles mainly focused on experienced drivers. This issue may be amplified for novice drivers with less driving skill. In this paper, we compared secondary-task engagement behaviors of novice and experienced drivers both in manual (non-automated) and automated driving settings in a driving simulator. A self-paced visual-manual secondary task presented on an in-vehicle display was utilized. Phase 1 of the study included 32 drivers (16 novice) who drove the simulator manually. In Phase 2, another set of 32 drivers (16 novice) drove with SAE-level-2 automation. In manual driving, there were no differences between novice and experienced drivers’ rate of manual interactions with the secondary task (i.e., taps on the display). However, with automation, novice drivers had a higher manual interaction rate with the task than experienced drivers. Further, experienced drivers had shorter average glance durations toward the task than novice drivers in general, but the difference was larger with automation compared with manual driving. It appears that with automation, experienced drivers are more conservative in their secondary-task engagement behaviors compared with novice drivers.


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