Secondary task engagement and disengagement in the context of highly automated driving

2018 ◽  
Vol 58 ◽  
pp. 253-263 ◽  
Author(s):  
Bernhard Wandtner ◽  
Nadja Schömig ◽  
Gerald Schmidt
Keyword(s):  
Secondary Task ◽  
Task Engagement ◽  
Automated Driving ◽  
Engagement And Disengagement ◽  
Highly Automated Driving

scholarly journals Highly Automated Driving, Secondary Task Performance, and Driver State

2012 ◽  
Vol 54 (5) ◽  
pp. 762-771 ◽  
Author(s):  
Natasha Merat ◽  
A. Hamish Jamson ◽  
Frank C. H. Lai ◽  
Oliver Carsten
Keyword(s):  
Task Performance ◽  
Secondary Task ◽  
Automated Driving ◽  
Highly Automated Driving

Influence of Driving Experience on Distraction Engagement in Automated Vehicles

2019 ◽  
Vol 2673 (9) ◽  
pp. 142-151 ◽  
Author(s):  
Dengbo He ◽  
Birsen Donmez
Keyword(s):  
Secondary Task ◽  
Driving Simulator ◽  
Phase 1 ◽  
Task Engagement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Novice Drivers ◽  
Driving Experience ◽  
Driving Skill ◽  
The Difference

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.

Performance in Takeover and Characteristics of Non-driving Related Tasks during Highly Automated Driving in Younger and Older Drivers

2017 ◽  
Vol 61 (1) ◽  
pp. 37-41 ◽  
Author(s):  
Hallie Clark ◽  
Anne Collins McLaughlin ◽  
Billy Williams ◽  
Jing Feng
Keyword(s):  
Response Time ◽  
Total Duration ◽  
Task Engagement ◽  
Older Drivers ◽  
Automated Driving ◽  
Average Speed ◽  
Subsequent Performance ◽  
Effect Of Age ◽  
Cognitive Dimensions ◽  
Highly Automated Driving

This paper aims to examine the effect of age and various characteristics of non-driving related activities during highly automated driving on subsequent performance in notified takeovers among younger and older drivers. The paper presents new analyses of data collected in our earlier study (Clark & Feng, 2016). Non-driving-related activities that participants voluntarily chose to engage in during automated driving were categorized according to their cognitive dimensions in information processing. Using hierarchical multiple regressions, we analyzed the effect of driver age, total duration and number of engagement in non-driving-related activities, the duration and cognitive dimensions of the last activity prior to takeover on average speed during takeover and the response time to a takeover notification. We found that older drivers speed was negatively predicted by age while their response time to a notification was not predicted by any factor. In contrast, younger drivers showed a trend of positive relationship between age and average speed and the characteristics of the last task engagement explained a significant portion of the variance of response time to a notification.

scholarly journals How Do Non-driving-related Tasks Affect Engagement Under Highly Automated Driving Situations? A Literature Review

2021 ◽  
Vol 2 ◽  
Author(s):  
Marie Jaussein ◽  
Lucie Lévêque ◽  
Jonathan Deniel ◽  
Thierry Bellet ◽  
Hélène Tattegrain ◽  
...  
Keyword(s):  
Literature Review ◽  
Task Engagement ◽  
Automated Driving ◽  
Global View ◽  
Driver Performance ◽  
Independent Variable ◽  
New Perspective ◽  
Technological Improvements ◽  
Transition Stages ◽  
Highly Automated Driving

Driving automation has become a trending topic over the past decade, as recent technical and technological improvements have created hope for a possible short-term release of partially automated vehicles. Several research teams have been exploring driver performance during control transitions performed under highly automated driving (i.e., while resuming manual driving, when facing a critical situation for instance). In this paper, we present a state of the art of studies dealing with control transitions as well as the concept of non-driving-related task (NDRT) engagement. More specifically, we aim to provide a global view on how task engagement is investigated in the literature. Two main utilisations of task engagement emerged from our literature review: its manipulation as independent variable to vary the driver’s engagement state before a control transition, and its measurement as dependent variable to compare its variation to driving behaviour variables during a control transition. Furthermore, we propose a new perspective on control transition, which was so far studied through a techno-centric approach; research works were indeed designed in function of the system state. Our article suggests a more cognitive-centred view by taking in account the evolution of engagement mechanisms along control transition stages. Finally, we provide a categorisation of engagement mechanisms’ variables involved during these different stages, with a view to facilitate future investigations on the driver’s engagement state during this crucial phase of highly automated driving.

scholarly journals Dynamic scan paths investigations under manual and highly automated driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jordan Navarro ◽  
Otto Lappi ◽  
François Osiurak ◽  
Emma Hernout ◽  
Catherine Gabaude ◽  
...  
Keyword(s):  
Relative Frequency ◽  
Human Locomotion ◽  
Visual Scanning ◽  
Automated Driving ◽  
The Gaze ◽  
Visual Inputs ◽  
Gaze Patterns ◽  
Gaze Behaviour ◽  
Scan Paths ◽  
Highly Automated Driving

AbstractActive visual scanning of the scene is a key task-element in all forms of human locomotion. In the field of driving, steering (lateral control) and speed adjustments (longitudinal control) models are largely based on drivers’ visual inputs. Despite knowledge gained on gaze behaviour behind the wheel, our understanding of the sequential aspects of the gaze strategies that actively sample that input remains restricted. Here, we apply scan path analysis to investigate sequences of visual scanning in manual and highly automated simulated driving. Five stereotypical visual sequences were identified under manual driving: forward polling (i.e. far road explorations), guidance, backwards polling (i.e. near road explorations), scenery and speed monitoring scan paths. Previously undocumented backwards polling scan paths were the most frequent. Under highly automated driving backwards polling scan paths relative frequency decreased, guidance scan paths relative frequency increased, and automation supervision specific scan paths appeared. The results shed new light on the gaze patterns engaged while driving. Methodological and empirical questions for future studies are discussed.

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