{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:18:43Z","timestamp":1776111523252,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52262047"],"award-info":[{"award-number":["52262047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52072214"],"award-info":[{"award-number":["52072214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20210214-1"],"award-info":[{"award-number":["20210214-1"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guilin Key R&amp;D Program","award":["52262047"],"award-info":[{"award-number":["52262047"]}]},{"name":"Guilin Key R&amp;D Program","award":["52072214"],"award-info":[{"award-number":["52072214"]}]},{"name":"Guilin Key R&amp;D Program","award":["20210214-1"],"award-info":[{"award-number":["20210214-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Ensuring the driver\u2019s readiness to take over before a takeover request is issued by an autonomous driving system is crucial for a safe takeover. However, current takeover prediction models suffer from poor prediction accuracy and do not consider the time dependence of input features. In this regard, this study proposes a hybrid LSTM-BiLSTM-ATTENTION algorithm for driver takeover performance prediction. By building a takeover scenario and conducting experiments in the driving simulation experimental platform under the human\u2013machine co-driving environment, the relevant state indicators in the 15 s per second before the takeover request is sent are extracted from three perspectives, namely, driver state, traffic environment, and personal attributes, as model inputs, and the level of takeover performance was labeled; the hybrid LSTM-BiLSTM-ATTENTION algorithm is used to construct a driver takeover performance prediction model and compare it with other five algorithms. The results show that the algorithm proposed in this study performs optimally, with an accuracy of 93.11%, a precision of 93.02%, a recall of 93.28%, and an F1 score of 93.12%. This study provides new ideas and methods for realizing the accurate prediction of driver takeover performance, and it can provide a decision basis for the safe design of self-driving vehicles.<\/jats:p>","DOI":"10.3390\/systems13010046","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T06:42:15Z","timestamp":1736750535000},"page":"46","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Driver Takeover Performance Prediction Based on LSTM-BiLSTM-ATTENTION Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Lijie","family":"Chen","sequence":"first","affiliation":[{"name":"College of Automotive Engineering, Guangxi Technological College of Machinery and Electricity, Nanning 530007, China"}]},{"given":"Daofei","family":"Li","sequence":"additional","affiliation":[{"name":"Information Management Center of Transportation, Nanning 530000, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Intelligent Transportation System, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-3712","authenticated-orcid":false,"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2326-4856","authenticated-orcid":false,"given":"Quan","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Safety and Energy, School of Vehicle & Mobility, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,11]]},"reference":[{"key":"ref_1","unstructured":"(2023, July 29). MIIT Wu Feng: Supporting the Commercialization of L3 and Higher Level Autonomous Driving. Available online: https:\/\/new.qq.com\/rain\/a\/20230929A02CU400."},{"key":"ref_2","unstructured":"(2023, May 29). Intelligent Vehicle (Smart Connected Vehicle) Industry Market Status and Trend Analysis. Available online: https:\/\/www.chinairn.com\/news\/20230529\/114729369.shtml?eqid=c0d20fe100044e8200000004647a3a4f."},{"key":"ref_3","unstructured":"Sun, B. (2020). Research on Personalized Shared Control Considering Driving Capability and Style. [Ph.D. Thesis, Jilin University]."},{"key":"ref_4","unstructured":"Ma, Y. (2020). Driving Distraction Detection in Co-Piloted Vehicles. [Master\u2019s Thesis, Wuhan University of Technology]."},{"key":"ref_5","unstructured":"(2021). Committee O-rad. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE Int."},{"key":"ref_6","first-page":"295","article-title":"Takeover transition in autonomous vehicles: A youtube study","volume":"36","author":"Zhou","year":"2020","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1109\/TIV.2022.3200826","article-title":"Human-vehicle interaction to support driver\u2019s situation awareness in automated vehicles: A systematic review","volume":"8","author":"Capallera","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1518\/001872095779064555","article-title":"The out-of-the-loop performance problem and level of control in automation","volume":"37","author":"Endsley","year":"1995","journal-title":"Hum. Factors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Radlmayr, J., Briich, K., Schmidt, K., Solbeck, C., and Wehner, T. (2018, January 1\u20135). Peripheral monitoring of traffic in conditionally automated driving. Proceedings of the 62nd Human Factors and Ergonomics Society Annual Meeting, HFES 2018, Philadelphia, PA, USA.","DOI":"10.1177\/1541931218621416"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.cstp.2018.07.004","article-title":"The situation awareness of young drivers, middle-aged drivers, and older drivers: Same but different?","volume":"8","author":"Jones","year":"2020","journal-title":"Case Stud. Transp. Policy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106143","DOI":"10.1016\/j.aap.2021.106143","article-title":"Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving","volume":"157","author":"Liang","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.trf.2022.04.018","article-title":"Impact of the driver\u2019s visual engagement on situation awareness and takeover quality","volume":"87","author":"Marti","year":"2022","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_13","unstructured":"Hong, H., Hu, Z., Rong, Y., Guo, C., and Fei, G. (2022). Driver situational awareness during the takeover process of L3 level autonomous driving. J. Jilin Univ., 1\u201312. Available online: https:\/\/kns.cnki.net\/kcms\/detail\/22.1341.T.20220802.1137.001.html."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103343","DOI":"10.1016\/j.apergo.2020.103343","article-title":"Modeling takeover time based on non-driving-related task attributes in highly automated driving","volume":"92","author":"Yoon","year":"2021","journal-title":"Appl. Ergon."},{"key":"ref_15","unstructured":"Lu, J. (2022). Prediction of Automated Driving Takeover Performance Under Different Cognitive Loads. [Master\u2019s Thesis, Harbin Institute of Technology]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102100","DOI":"10.1016\/j.aei.2023.102100","article-title":"Takeover performance prediction of driver physiological state of different cognitive tasks in conditionally automated drivin","volume":"57","author":"Zhu","year":"2023","journal-title":"Adv. Eng. Inform."},{"key":"ref_17","unstructured":"Pakdamanian, E., Sheng, S., Baee, S., Heo, S., Kraus, S., and Feng, L. (2021, January 8\u201313). Deeptake: Prediction of autonomous driving under different cognitive load. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105748","DOI":"10.1016\/j.aap.2020.105748","article-title":"Predicting driver takeover performance in conditionally automated drivin","volume":"148","author":"Du","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TIV.2019.2955364","article-title":"Looking at the driver\/rider in autonomous vehicles to predict take-over readiness","volume":"5","author":"Deo","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1177\/1071181322661303","article-title":"Modeling driver takeover intention in automated vehicles with attention-based CNN algorithm","volume":"66","author":"Gupta","year":"2022","journal-title":"Proc. Hum. Factors Ergon. Soc. Annu. Meet."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106593","DOI":"10.1016\/j.aap.2022.106593","article-title":"The effects of takeover request lead time on drivers\u2019 situation awareness for manually exiting from freeways: A web-based study on level 3 automated vehicles","volume":"168","author":"Tan","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107068","DOI":"10.1016\/j.aap.2023.107068","article-title":"Supervising the self-driving car: Situation awareness and fatigue during highly automated driving","volume":"187","author":"Mckerral","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3590","DOI":"10.1109\/JSYST.2019.2918283","article-title":"A review of situation awareness assessment approaches in aviation environment","volume":"13","author":"Nguyen","year":"2019","journal-title":"IEEE Syst. J."},{"key":"ref_24","unstructured":"Zhi, L., and Wen, Z. (2020). Transportation Big Data: Theory and Methods, Zhejiang University Press."},{"key":"ref_25","first-page":"2022","article-title":"Review of Take-over Performance of Autonomous Driving: Influencing Factors, Models, and Evaluation Methods","volume":"36","author":"Wen","year":"2023","journal-title":"China J. Highw. Transp."},{"key":"ref_26","unstructured":"Yang, X. (2022). Research on Driving Distraction Recognition Model Based on LSTM-PEEPHOLE. [Master\u2019s Thesis, Jiangsu University]."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, T., Li, S., and Zhang, J. (2023, January 20\u201322). Short-time prediction of parking demand based on LSTM neural network model. Proceedings of the 2023 International Conference on Automation Control, Chongqing, China.","DOI":"10.1117\/12.2686377"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"124384","DOI":"10.1016\/j.energy.2022.124384","article-title":"Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model","volume":"254","author":"Niu","year":"2022","journal-title":"Energy"},{"key":"ref_29","first-page":"1135","article-title":"Short-term wind power prediction method based on data stabilization and BiLSTM","volume":"35","author":"Xian","year":"2023","journal-title":"J. Chongqing Univ. Posts Telecommun. (Nat. Sci. Ed.)"},{"key":"ref_30","unstructured":"Hong, M., Jian, Z., Zhao, C., and Li, C. (2024). Fault diagnosis of DC microgrid based on CNN-BiLSTM-Attention. Chin. J. Electr. Eng., 1\u201312."},{"key":"ref_31","unstructured":"Pei, Y., Hua, H., Wei, W., and Bao, G. (2023). Improved PSO-BiLSTM tool wear prediction based on attention mechanis. J. Beijing Univ. Aeronaut. Astronaut., 1\u201312."},{"key":"ref_32","unstructured":"Xin, L., Wei, C., Wen, C., Yu, K., Yu, J., and Shao, J. (2023). Research on Pilot Situational Awareness Recognition Based on Eye Tracking Technology. J. Saf. Environ., 1\u201311."},{"key":"ref_33","unstructured":"TOBII (2023, May 29). Tobii Pro Glasses 3 User Manual. Available online: https:\/\/www.tobii.com\/products\/eye-trackers\/wearables\/tobii-pro-glasses-3."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1177\/0018720819829572","article-title":"Toward computational simulations of behavior during automated driving takeovers: A review of the empirical and modeling literatures","volume":"61","author":"Mcdonald","year":"2019","journal-title":"Hum. Factors"},{"key":"ref_35","first-page":"352","article-title":"Take over gradually in conditional automated driving: The effect of two-stage warning systems on situation awareness, driving stress, takeover performance, and acceptance","volume":"37","author":"Ma","year":"2021","journal-title":"Int. J. Hum.-Comput. Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.trf.2019.03.018","article-title":"Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance","volume":"63","author":"Lu","year":"2019","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_37","unstructured":"Peng, M. (2020). Study on the Takeover Risk of Autonomous Vehicles Considering Multiple Risk Source. [Master\u2019s Thesis, Chongqing University]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12544-021-00475-5","article-title":"Effects of non-driving related tasks on mental workload and take-over times during conditional automated driving","volume":"13","author":"Muller","year":"2021","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ssci.2012.11.004","article-title":"Missing links? The effects of distraction on driver situation awareness","volume":"56","author":"Young","year":"2013","journal-title":"Saf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.trf.2020.08.018","article-title":"Overall performance impairment and crash risk due to distracted driving: A comprehensive analysis using structural equation modelling","volume":"74","author":"Choudhary","year":"2020","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_41","first-page":"135","article-title":"A Study on the Impact of Immersion Levels of Non-driving-related Tasks on Takeover Behavior","volume":"40","author":"Yan","year":"2022","journal-title":"J. Transp. Inf. Saf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.chb.2005.12.009","article-title":"On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state","volume":"22","author":"Bailey","year":"2006","journal-title":"Comput. Hum. Behav."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107018","DOI":"10.1016\/j.aap.2023.107018","article-title":"Impact of duration of monitoring before takeover request on takeover time with insights into eye tracking data","volume":"185","author":"Huang","year":"2023","journal-title":"Accid. Anal. Prev."},{"key":"ref_44","first-page":"240","article-title":"Performance Analysis of Autonomous Vehicle Takeover in Urban Road Environment","volume":"32","author":"Qing","year":"2019","journal-title":"China J. Highw. Transp."},{"key":"ref_45","unstructured":"Molnar, L. (2017). Age-Related Differences in Driver Behavior Associated with Automated Vehicles and the Transfer of Control between Automated and Manual Control: A Simulator Evaluation, Transportation Research Institute, University of Michigan. Technical Report."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zeng, Y.L., Yang, Z., Kang, C.Y., Wu, C.X., Shi, J.L., Ma, S., and Li, H.T. (2021). Optimal time intervals in two-stage takeover warning systems with insight into the drivers\u2019 neuroticism personality. Front. Psychol., 12.","DOI":"10.3389\/fpsyg.2021.601536"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.trf.2021.10.004","article-title":"Evaluating the impacts of driver\u2019s pre-warning cognitive state on takeover performance under conditional automation","volume":"83","author":"Agrawal","year":"2021","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.trf.2023.04.012","article-title":"Study on the influence factors of takeover behavior in automated driving based on survival analysis","volume":"95","author":"Chen","year":"2023","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wintersberger, P., Riener, A., Schartmuller, C., Frison, A.K., and Weigl, K. (2018, January 23\u201325). Let me finish before I take over: Towards attention aware device integration in highly automated vehicles. Proceedings of the 10th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2018, Toronto, ON, Canada.","DOI":"10.1145\/3239060.3239085"},{"key":"ref_50","first-page":"159","article-title":"Takeover-performance and Takeover-risk Evaluation Under Non-critical Transition Scenarios","volume":"35","author":"Xiao","year":"2022","journal-title":"China J. Highw. Transp."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, Q., Chen, H., Gong, J., Zhao, X., and Li, Z. (2022). Studying driver\u2019s perception arousal and takeover performance in autonomous driving. Sustainability, 15.","DOI":"10.3390\/su15010445"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/1\/46\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:27:05Z","timestamp":1759919225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/1\/46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,11]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["systems13010046"],"URL":"https:\/\/doi.org\/10.3390\/systems13010046","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,11]]}}}