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Affiliation
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a Dept. of Intelligent Mechanical Systems , Graduate School of Natural Science and Technology, Okayama University , Okayama , Japan. |
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Abstract
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OBJECTIVE: This study aimed at identifying and predicting in advance the point in time with high risk of virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure.
METHODS: The behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted by using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with high risk of virtual accident (in real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of virtual accident).
RESULTS: On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with high risk of virtual accident before the point in time of virtual accident.
CONCLUSION: The proposed method is one of the promising techniques for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving, and for warning drivers of such a drowsy and risky state.
Language: en |