Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function
Healthcare for truck drivers is an important issue. To prevent fatigue-related collisions among drivers, objective assessments of their physiological states are essential. A simple and quantitative evaluation method for fatigue involves the use of autonomic nerve function (ANF) indices obtained from...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10186273/ |
| _version_ | 1797688433703911424 |
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| author | Nao Ito Shunsuke Minusa Takeshi Tanaka Yun Li Hiroyuki Kuriyama |
| author_facet | Nao Ito Shunsuke Minusa Takeshi Tanaka Yun Li Hiroyuki Kuriyama |
| author_sort | Nao Ito |
| collection | DOAJ |
| description | Healthcare for truck drivers is an important issue. To prevent fatigue-related collisions among drivers, objective assessments of their physiological states are essential. A simple and quantitative evaluation method for fatigue involves the use of autonomic nerve function (ANF) indices obtained from heart rate variability analysis. However, predicting the occurrence of crashes using only physiological data is challenging. In most previous studies, the targets of driving situations have been often limited, or the prediction targets have been set as the driver’s internal state rather than the accident. In this paper, we propose a novel collision risk prediction model using ANF and several simple external information types, which can be extracted from standard in-vehicle sensors without limiting the driving scene. Our experiments using actual truck drivers’ data reveal that the proposed model can achieve collision risk prediction for the following 30 min with an accuracy of 74.9% recall and 0.79 AUC. Furthermore, we discover that simple external information obtained based on the vehicle speed significantly contributes to the prediction accuracy. As our prediction method only requires commonly equipped sensors as the sources of external information, this method is expected to be easily implemented not only for truck driving but also for general vehicle driving, where crashes are often likely. |
| first_indexed | 2024-03-12T01:31:58Z |
| format | Article |
| id | doaj.art-230fbedfb26a48fcb75ba504ab23e303 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:31:58Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-230fbedfb26a48fcb75ba504ab23e3032023-09-11T23:02:42ZengIEEEIEEE Access2169-35362023-01-0111942189422610.1109/ACCESS.2023.329631410186273Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve FunctionNao Ito0https://orcid.org/0000-0002-0929-4418Shunsuke Minusa1https://orcid.org/0000-0002-8186-3603Takeshi Tanaka2Yun Li3Hiroyuki Kuriyama4Research and Development Group, Hitachi Ltd., Tokyo, JapanResearch and Development Group, Hitachi Ltd., Tokyo, JapanResearch and Development Group, Hitachi Ltd., Tokyo, JapanResearch and Development Group, Hitachi Ltd., Tokyo, JapanResearch and Development Group, Hitachi Ltd., Tokyo, JapanHealthcare for truck drivers is an important issue. To prevent fatigue-related collisions among drivers, objective assessments of their physiological states are essential. A simple and quantitative evaluation method for fatigue involves the use of autonomic nerve function (ANF) indices obtained from heart rate variability analysis. However, predicting the occurrence of crashes using only physiological data is challenging. In most previous studies, the targets of driving situations have been often limited, or the prediction targets have been set as the driver’s internal state rather than the accident. In this paper, we propose a novel collision risk prediction model using ANF and several simple external information types, which can be extracted from standard in-vehicle sensors without limiting the driving scene. Our experiments using actual truck drivers’ data reveal that the proposed model can achieve collision risk prediction for the following 30 min with an accuracy of 74.9% recall and 0.79 AUC. Furthermore, we discover that simple external information obtained based on the vehicle speed significantly contributes to the prediction accuracy. As our prediction method only requires commonly equipped sensors as the sources of external information, this method is expected to be easily implemented not only for truck driving but also for general vehicle driving, where crashes are often likely.https://ieeexplore.ieee.org/document/10186273/Autonomic nerve functioncollisiondeep learningfatigueheart rate variabilitylong short-term memory |
| spellingShingle | Nao Ito Shunsuke Minusa Takeshi Tanaka Yun Li Hiroyuki Kuriyama Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function IEEE Access Autonomic nerve function collision deep learning fatigue heart rate variability long short-term memory |
| title | Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function |
| title_full | Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function |
| title_fullStr | Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function |
| title_full_unstemmed | Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function |
| title_short | Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function |
| title_sort | prediction of future collision risk for truck drivers using the time series autonomic nerve function |
| topic | Autonomic nerve function collision deep learning fatigue heart rate variability long short-term memory |
| url | https://ieeexplore.ieee.org/document/10186273/ |
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