{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T01:49:01Z","timestamp":1769737741637,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Society","award":["21K17781"],"award-info":[{"award-number":["21K17781"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of automated driving, inferring a driver\u2019s behavior can be a key element for designing an Advanced Driver Assistance System (ADAS). Current research is focused on describing and predicting a driver\u2019s behaviors as labels, e.g., lane shifting, lane keeping, etc., during driving. In our work, we consider that predicting a driver\u2019s behavior can be described as predicting a trajectory the driver may follow in the near future. The target trajectory can be calculated through certain polynomial functions. Via the data set collected by a Driving Simulator experiment covering nine volunteers, we proposed a model based on a deep learning network which is capable of predicting the corresponding coefficients of polynomial functions and then generating the trajectories in the next few seconds. The results also discussed and analyzed some possible factors affecting the prediction error. In conclusion, the model proved to be effective in predicting the target trajectory of a driver.<\/jats:p>","DOI":"10.3390\/s23031405","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T01:27:58Z","timestamp":1674782878000},"page":"1405","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Predictive Model of a Driver\u2019s Target Trajectory Based on Estimated Driving Behaviors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4042-7573","authenticated-orcid":false,"given":"Zhanhong","family":"Yan","sequence":"first","affiliation":[{"name":"The Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8976-5971","authenticated-orcid":false,"given":"Bo","family":"Yang","sequence":"additional","affiliation":[{"name":"The Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7589-7954","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"The Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kimihiko","family":"Nakano","sequence":"additional","affiliation":[{"name":"The Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2015). Global Status Report on Road Safety, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106165","DOI":"10.1016\/j.aap.2021.106165","article-title":"Driver Influence on Vehicle Trajectory Prediction","volume":"157","author":"Khakzar","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TITS.2009.2026675","article-title":"On the Roles of Eye Gaze and Head Dynamics in Predicting Driver\u2019s Intent to Change Lanes","volume":"10","author":"Doshi","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.neucom.2013.04.035","article-title":"A Comparison of Selected Simple Supervised Learning Algorithms to Predict Driver Intent Based on Gaze Data","volume":"121","author":"Lethaus","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/THMS.2017.2693230","article-title":"The Effect of a Haptic Guidance Steering System on Fatigue-Related Driver Behavior","volume":"47","author":"Wang","year":"2017","journal-title":"IEEE Trans. Human-Machine Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M.M., and Kim, H.S. (2022). Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey. Mathematics, 10.","DOI":"10.3390\/math10183233"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yan, Z., Yang, K., Wang, Z., Yang, B., Kaizuka, T., and Nakano, K. (2019, January 9\u201312). Time to Lane Change and Completion Prediction Based on Gated Recurrent Unit Network. Proceedings of the IEEE Intelligent Vehicles Symposium, Paris, France.","DOI":"10.1109\/IVS.2019.8813838"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1109\/TIV.2020.3044180","article-title":"Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System","volume":"6","author":"Yan","year":"2021","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.aap.2016.06.017","article-title":"Cost and Benefit Estimates of Partially-Automated Vehicle Collision Avoidance Technologies","volume":"95","author":"Harper","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kumar, P., Perrollaz, M., Lefevre, S., and Laugier, C. (2013, January 15\u201317). Learning-Based Approach for Online Lane Change Intention Prediction. Proceedings of the IEEE Intelligent Vehicles Symposium, London, UK.","DOI":"10.1109\/IVS.2013.6629564"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, I.H., Bong, J.H., Park, J., and Park, S. (2017). Prediction of Driver\u2019s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques. Sensors, 17.","DOI":"10.3390\/s17061350"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dang, H.Q., F\u00fcrnkranz, J., Biedermann, A., and Hoepfl, M. (2018, January 4\u20137). Time-to-Lane-Change Prediction with Deep Learning. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Maui, HI, USA.","DOI":"10.1109\/ITSC.2017.8317674"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Messaoud, K., Deo, N., Trivedi, M.M., and Nashashibi, F. (2021, January 11\u201317). Trajectory Prediction for Autonomous Driving Based on Multi-Head Attention with Joint Agent-Map Representation. Proceedings of the IEEE Intelligent Vehicles Symposium, Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9576054"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Park, S.H., Kim, B., Kang, C.M., Chung, C.C., and Choi, J.W. (2018, January 26\u201330). Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture. Proceedings of the IEEE Intelligent Vehicles Symposium, Changshu, China.","DOI":"10.1109\/IVS.2018.8500658"},{"key":"ref_15","unstructured":"Sohn, K., Yan, X., and Lee, H. (2015, January 7\u201310). Learning Structured Output Representation Using Deep Conditional Generative Models. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"21897","DOI":"10.1109\/ACCESS.2020.2968618","article-title":"A Dual Learning Model for Vehicle Trajectory Prediction","volume":"8","author":"Khakzar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ssci.2019.01.016","article-title":"Second Strategic Highway Research Program Naturalistic Driving Study Methods","volume":"119","author":"Antin","year":"2019","journal-title":"Saf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8720","DOI":"10.1109\/TVT.2021.3098429","article-title":"Trajectory Prediction of Preceding Target Vehicles Based on Lane Crossing and Final Points Generation Model Considering Driving Styles","volume":"70","author":"Liu","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","unstructured":"Schubert, R., Richter, E., and Wanielik, G. (2008, January 30\u201331). Comparison and Evaluation of Advanced Motion Models for Vehicle Tracking. Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, Y., Zhan, W., and Tomizuka, M. (2018, January 26\u201330). Probabilistic Prediction of Vehicle Semantic Intention and Motion. Proceedings of the IEEE Intelligent Vehicles Symposium, Changshu, China.","DOI":"10.1109\/IVS.2018.8500419"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H.S., and Chandraker, M. (2017, January 21\u201326). DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents Namhoon. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, T., Xu, Y., Monfort, M., Choi, W., Baker, C., Zhao, Y., Wang, Y., and Wu, Y.N. (2019, January 16\u201320). Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9846","DOI":"10.1109\/TIE.2019.2893864","article-title":"Cooperative Trajectory Planning for Haptic Shared Control between Driver and Automation in Highway Driving","volume":"66","author":"Benloucif","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Houenou, A., Bonnifait, P., Cherfaoui, V., and Yao, W. (2013, January 3\u20138). Vehicle Trajectory Prediction Based on Motion Model and Maneuver Recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696982"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1109\/TITS.2010.2046037","article-title":"Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road with Traffic and Driver Interaction","volume":"11","author":"Glaser","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2022, November 10). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Available online: https:\/\/arxiv.org\/abs\/:1412.3555."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TIV.2018.2886654","article-title":"Relationship between Gaze Behavior and Steering Performance for Driver-Automation Shared Control: A Driving Simulator Study","volume":"4","author":"Wang","year":"2019","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_29","unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2022, November 10). A Critical Review of Recurrent Neural Networks for Sequence Learning. Available online: https:\/\/arxiv.org\/abs\/1506.00019."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, S., Yu, X., and Zhou, Y. (2020, January 12\u201314). LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example. Proceedings of the Proceedings\u20142020 International Workshop on Electronic Communication and Artificial Intelligence, IWECAI 2020, Shanghai, China.","DOI":"10.1109\/IWECAI50956.2020.00027"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1405\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:16:37Z","timestamp":1760120197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,26]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031405"],"URL":"https:\/\/doi.org\/10.3390\/s23031405","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,26]]}}}