Driver Support Functions Under Resource-Limited Situations

2007 ◽  
Vol 51 (4) ◽  
pp. 176-180 ◽  
Author(s):  
T. Inagaki ◽  
M. Itoh ◽  
Y. Nagai
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Reasoning Under Uncertainty ◽  
Support Functions ◽  
Traffic Condition ◽  
Monitoring Method ◽  
Safety Control ◽  
Resource Limited ◽  
Action Type ◽  
Driver Support

What type of support should be given to an automobile driver when it is determined, via some monitoring method, that the driver's situation awareness may not be appropriate to a given traffic condition? With a driving simulator, the following three conditions were compared: (a) Warning type support in which an auditory warning is given to the driver to enhance situation awareness, (b) action type support in which an autonomous safety control action is executed to avoid an accident, and (c) the no-aid baseline condition. Although the both types of driver support are effective, the warning type support sometimes fail to assure safety, which suggests a limitation of the human locus of control assumption. Efficacy of the action type support can also be degraded due to a characteristic of human reasoning under uncertainty. This paper discusses viewpoints needed in the design of systems for supporting drivers in resource-limited situations.

Effects of Cognitive Distraction on Checking Traffic Conditions for Changing Lanes

2009 ◽  
Vol 53 (12) ◽  
pp. 824-828 ◽  
Author(s):  
Huiping Zhou ◽  
Makoto Itoh ◽  
Toshiyuki Inagaki
Keyword(s):  
Situation Awareness ◽  
Cognitive Task ◽  
Support Functions ◽  
Traffic Condition ◽  
Lane Changes ◽  
Traffic Conditions ◽  
Cognitive Distraction ◽  
Time Period ◽  
Long Time ◽  
Driving Task

This paper aimed to reveal effects of cognitively distracting activity on checking traffic condition before changing lanes. We conducted an experiment to investigate driver behavior to change lanes under two conditions: only a driving task and an additional cognitive task. It was revealed that the decrease and delay on checking traffic occurred continually during a long time period before executing lane changes, not just temporarily. The result showed that distraction might contribute to the effects. It was also suggested that cognitive distraction may degrade the perceptual capability in situation awareness. A necessary was demonstrated to give support functions, which aid a driver enhancing situation awareness and attract driver's attention from distractions, in order to prevent accidents in lane changes.

scholarly journals Driver Support Functions under Resource-Limited Situations

2008 ◽  
Vol 1 (2) ◽  
pp. 213-222 ◽  
Author(s):  
Toshiyuki INAGAKI ◽  
Makoto ITOH ◽  
Yoshitomo NAGAI
Keyword(s):  
Support Functions ◽  
Resource Limited ◽  
Driver Support

scholarly journals Effects of mental demands on situation awareness during platooning: A driving simulator study

2018 ◽  
Vol 58 ◽  
pp. 193-209 ◽  
Author(s):  
Daniël D. Heikoop ◽  
Joost C.F. de Winter ◽  
Bart van Arem ◽  
Neville A. Stanton
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Mental Demands ◽  
Simulator Study

scholarly journals Occupant Monitoring in Automated Vehicles: Classification of Situation Awareness Based on Head Movements While Cornering

2019 ◽  
Vol 63 (1) ◽  
pp. 2078-2082
Author(s):  
Frederik Schewe ◽  
Hao Cheng ◽  
Alexander Hafner ◽  
Monika Sester ◽  
Mark Vollrath
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Head Tilt ◽  
Head Movements ◽  
Automated Driving ◽  
The Road ◽  
Subject Design ◽  
Visual Reference ◽  
The Mean

We tested whether head-movements under automated driving can be used to classify a vehicle occupant as either situation-aware or unaware. While manually cornering, an active driver’s head tilt correlates with the road angle which serves as a visual reference, whereas an inactive passenger’s head follows the g-forces. Transferred to partial/conditional automation, the question arises whether aware occupant’s head-movements are comparable to drivers and if this can be used for classification. In a driving-simulator-study (n=43, within-subject design), four scenarios were used to generate or deteriorate situation awareness (manipulation checked). Recurrent neural networks were trained with the resulting head-movements. Inference statistics were used to extract the discriminating feature, ensuring explainability. A very accurate classification was achieved and the mean side rotation-rate was identified as the most differentiating factor. Aware occupants behave more like drivers. Therefore, head-movements can be used to classify situation awareness in experimental settings but also in real driving.

scholarly journals Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

Safety ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 34
Author(s):  
Shi Cao ◽  
Pinyan Tang ◽  
Xu Sun
Keyword(s):  
Reaction Time ◽  
Interior Design ◽  
Situation Awareness ◽  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Maximum Magnitude ◽  
Significant Difference ◽  
Driving Conditions

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

scholarly journals An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

10.3791/51242 ◽  
2014 ◽  
Author(s):  
Justen Manasa ◽  
Siva Danaviah ◽  
Sureshnee Pillay ◽  
Prevashinee Padayachee ◽  
Hloniphile Mthiyane ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Resource Limited Settings ◽  
Monitoring Method ◽  
Resistance Monitoring ◽  
Resource Limited ◽  
Hiv 1
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