EEG-based emergency braking intention detection during simulated driving
Abstract Background Current research related to electroencephalogram (EEG)-based driver’s emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-023-01129-4 |
| _version_ | 1827910738590564352 |
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| author | Xinbin Liang Yang Yu Yadong Liu Kaixuan Liu Yaru Liu Zongtan Zhou |
| author_facet | Xinbin Liang Yang Yu Yadong Liu Kaixuan Liu Yaru Liu Zongtan Zhou |
| author_sort | Xinbin Liang |
| collection | DOAJ |
| description | Abstract Background Current research related to electroencephalogram (EEG)-based driver’s emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features. Methods To this end, a novel EEG-based driver’s emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input. Results We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking. Conclusions The study provides a user-centered framework for human–vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions. |
| first_indexed | 2024-03-13T01:53:53Z |
| format | Article |
| id | doaj.art-d974ed0a9df24043ba26466125c8d719 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-13T01:53:53Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-d974ed0a9df24043ba26466125c8d7192023-07-02T11:21:30ZengBMCBioMedical Engineering OnLine1475-925X2023-07-0122112010.1186/s12938-023-01129-4EEG-based emergency braking intention detection during simulated drivingXinbin Liang0Yang Yu1Yadong Liu2Kaixuan Liu3Yaru Liu4Zongtan Zhou5College of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologyAbstract Background Current research related to electroencephalogram (EEG)-based driver’s emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features. Methods To this end, a novel EEG-based driver’s emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input. Results We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking. Conclusions The study provides a user-centered framework for human–vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.https://doi.org/10.1186/s12938-023-01129-4Emergency braking intentionElectroencephalogram (EEG)DetectionSimulated drivingBrain-computer interface (BCI) |
| spellingShingle | Xinbin Liang Yang Yu Yadong Liu Kaixuan Liu Yaru Liu Zongtan Zhou EEG-based emergency braking intention detection during simulated driving BioMedical Engineering OnLine Emergency braking intention Electroencephalogram (EEG) Detection Simulated driving Brain-computer interface (BCI) |
| title | EEG-based emergency braking intention detection during simulated driving |
| title_full | EEG-based emergency braking intention detection during simulated driving |
| title_fullStr | EEG-based emergency braking intention detection during simulated driving |
| title_full_unstemmed | EEG-based emergency braking intention detection during simulated driving |
| title_short | EEG-based emergency braking intention detection during simulated driving |
| title_sort | eeg based emergency braking intention detection during simulated driving |
| topic | Emergency braking intention Electroencephalogram (EEG) Detection Simulated driving Brain-computer interface (BCI) |
| url | https://doi.org/10.1186/s12938-023-01129-4 |
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