Abstract
The behavioral measures such as neck vending angle and tracking error in steering maneuvering during the simulated driving task was recorded under the low arousal condition of all participants who stayed up all night without sleeping. We conducted trend analysis where time and the behavioral measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data of participants from whom the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (virtual accident), we proposed a method to predict in advance (before virtual accident occurs) the point in time with high risk of crash. By applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could identify the point in time with high risk of crash and eventually predict in advance the symptom of the occurrence of point in time of virtual accident.
| Original language | English |
|---|---|
| Pages (from-to) | 2434-2441 |
| Number of pages | 8 |
| Journal | Procedia Manufacturing |
| Volume | 3 |
| DOIs | |
| Publication status | Published - 2015 |
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Keywords
- Behavioral measures
- Point in time of crash
- Prediction of drowsiness
- Trend analysis
ASJC Scopus subject areas
- Artificial Intelligence
- Industrial and Manufacturing Engineering
Cite this
Trend Analysis of Behavioral Measures for Predicting Point in Time of Crash. / Murata, Atsuo; Yamaashi, Takashi; Fukuda, Kohei; Moriwaka, Makoto.
In: Procedia Manufacturing, Vol. 3, 2015, p. 2434-2441.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Trend Analysis of Behavioral Measures for Predicting Point in Time of Crash
AU - Murata, Atsuo
AU - Yamaashi, Takashi
AU - Fukuda, Kohei
AU - Moriwaka, Makoto
PY - 2015
Y1 - 2015
N2 - The behavioral measures such as neck vending angle and tracking error in steering maneuvering during the simulated driving task was recorded under the low arousal condition of all participants who stayed up all night without sleeping. We conducted trend analysis where time and the behavioral measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data of participants from whom the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (virtual accident), we proposed a method to predict in advance (before virtual accident occurs) the point in time with high risk of crash. By applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could identify the point in time with high risk of crash and eventually predict in advance the symptom of the occurrence of point in time of virtual accident.
AB - The behavioral measures such as neck vending angle and tracking error in steering maneuvering during the simulated driving task was recorded under the low arousal condition of all participants who stayed up all night without sleeping. We conducted trend analysis where time and the behavioral measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data of participants from whom the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (virtual accident), we proposed a method to predict in advance (before virtual accident occurs) the point in time with high risk of crash. By applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could identify the point in time with high risk of crash and eventually predict in advance the symptom of the occurrence of point in time of virtual accident.
KW - Behavioral measures
KW - Point in time of crash
KW - Prediction of drowsiness
KW - Trend analysis
UR - http://www.scopus.com/inward/record.url?scp=85009958125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009958125&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2015.07.503
DO - 10.1016/j.promfg.2015.07.503
M3 - Article
AN - SCOPUS:85009958125
VL - 3
SP - 2434
EP - 2441
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
ER -