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á à Á À é è É È ç Ç ê
Ä Ö Ü ä ö ü ß
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      저자 : Kazuki Saruta (검색결과 2 건)
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      • Readiness Estimation for a Take-Over Request in Automated Driving on an Expressway

        Ryohei Suzuki,Hirokazu Madokoro,Stephanie Nix,Kazuki Saruta,Takashi K. Saito,Kazuhito Sato 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

        Automated driving is attracting attention as a solution to road traffic problems. At Level 3, a take-over request (TOR) is issued to transfer driving operations from the system to a driver when it is unable to continue. In such cases, the driver must be monitored to ensure a proper takeover of the driving operations. This study aims to measure drivers’ brain activity before and after the TOR by analyzing time-series signals of brain activity with machine learning algorithms. We developed driving scenarios with a TOR trigger on a rainy expressway at night. We used a portable functional near-infrared spectroscopy (fNIRS) device to measure cerebral blood oxygenation changes (ΔHbO) at the frontal pole. We used a long short-term memory (LSTM) network on this data for time-series learning and prediction after multivariate and multilayering modifications to improve accuracy. We conducted driving questionnaires beforehand and used two classification methods to categorize subjects into several groups with similar driving characteristics. Experimental results of a ΔHbO drop revealed that brain activity tended to decrease during automated driving. Moreover, success in obstacle avoidance and mean squared error (MSE) for each driver group demonstrated that the behavior toward an obstacle after the TOR trigger influenced changes in brain activity.

      • Prediction of Time-Series Brain Activity Changes Before and After Near-Miss Events in Snow Traffic Conditions

        Yuto Shoji,Hirokazu Madokoro,Stephanie Nix,Kazuki Saruta,Takashi K. Saito,Kazuhito Sato 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

        The number of accidents involving pedestrians and bicyclists has been reported to be about 1.8 times higher on narrow roads than on arterial roads in Japan. We consider investigating the circumstances under which accidents occur on narrow roads to be an important research task. Statistics from the Tokyo Metropolitan Police Department indicate that the number of traffic accident fatalities in winter is relatively high. We used a Driving Simulator (DS) in order to safely perform sensing on roads that replicate a local city in a heavy snowfall region. Brain activity during driving was measured using a portable functional Near-Infrared Spectroscopy (fNIRS) device. We used a machine-learning algorithm for analyzing time-series datasets to demonstrate differences in brain activity across driving events. We classified subjects into four groups based on the results of questionnaires that assessed their driving characteristics. Experimentally obtained results demonstrated that Root Mean Squared Error (RMSE) changes that represent increased brain activity were greater in winter than in summer for each event. We infer that the winter events had a larger impact on the drivers.

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