{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:19:05Z","timestamp":1776838745365,"version":"3.51.2"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"key-Area Research and Development Program of Guangdong Province","award":["2020B0909050003"],"award-info":[{"award-number":["2020B0909050003"]}]},{"name":"key-Area Research and Development Program of Guangdong Province","award":["CJGJZD20200617102801005"],"award-info":[{"award-number":["CJGJZD20200617102801005"]}]},{"name":"Science and Technology Innovation Committee of Shenzhen","award":["2020B0909050003"],"award-info":[{"award-number":["2020B0909050003"]}]},{"name":"Science and Technology Innovation Committee of Shenzhen","award":["CJGJZD20200617102801005"],"award-info":[{"award-number":["CJGJZD20200617102801005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Virtual testing requires hazardous scenarios to effectively test autonomous vehicles (AVs). Existing studies have obtained rarer events by sampling methods in a fixed scenario space. In reality, heterogeneous drivers behave differently when facing the same situation. To generate more realistic and efficient scenarios, we propose a two-stage heterogeneous driver model to change the number of dangerous scenarios in the scenario space. We trained the driver model using the HighD dataset, and generated scenarios through simulation. Simulations were conducted in 20 experimental groups with heterogeneous driver models and 5 control groups with the original driver model. The results show that, by adjusting the number and position of aggressive drivers, the percentage of dangerous scenarios was significantly higher compared to that of models not accounting for driver heterogeneity. To further verify the effectiveness of our method, we evaluated two driving strategies: car-following and cut-in scenarios. The results verify the effectiveness of our approach. Cumulatively, the results indicate that our approach could accelerate the testing of AVs.<\/jats:p>","DOI":"10.3390\/s23094570","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T01:34:26Z","timestamp":1683596066000},"page":"4570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Scenario Generation for Autonomous Vehicles with Deep-Learning-Based Heterogeneous Driver Models: Implementation and Verification"],"prefix":"10.3390","volume":"23","author":[{"given":"Li","family":"Gao\u00a0","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China"},{"name":"Research Institute of Tsinghua, Pearl River Delta, Guangzhou 510530, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-5512","authenticated-orcid":false,"given":"Rui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China"},{"name":"Waytous Inc., Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5826-3765","authenticated-orcid":false,"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China"},{"name":"Research Institute of Tsinghua, Pearl River Delta, Guangzhou 510530, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, S., and Capretz, L.F. (2021, January 9\u201312). An analysis of testing scenarios for automated driving systems. Proceedings of the 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Honolulu, HI, USA.","DOI":"10.1109\/SANER50967.2021.00078"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TIV.2016.2608003","article-title":"Intelligence testing for autonomous vehicles: A new approach","volume":"1","author":"Li","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"eaaw4106","DOI":"10.1126\/scirobotics.aaw4106","article-title":"Parallel testing of vehicle intelligence via virtual-real interaction","volume":"4","author":"Li","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TIV.2022.3196396","article-title":"Verification and validation methods for decision-making and planning of automated vehicles: A review","volume":"7","author":"Ma","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TIV.2022.3179104","article-title":"Verification and validation of intelligent vehicles: Objectives and efforts from china","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1109\/JRFID.2022.3223092","article-title":"Genetic algorithm-based challenging scenarios generation for autonomous vehicle testing","volume":"6","author":"Zhou","year":"2022","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6297","DOI":"10.1109\/TITS.2020.2991039","article-title":"A theoretical foundation of intelligence testing and its application for intelligent vehicles","volume":"22","author":"Li","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TITS.2016.2582208","article-title":"Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques","volume":"18","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1109\/TITS.2017.2766172","article-title":"Accelerated evaluation of automated vehicles using piecewise mixture models","volume":"19","author":"Huang","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Althoff, M., and Lutz, S. (2018, January 26\u201330). Automatic generation of safety-critical test scenarios for collision avoidance of road vehicles. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500374"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14088","DOI":"10.1109\/TITS.2021.3136353","article-title":"Scenario-based test automation for highly automated vehicles: A review and paving the way for systematic safety assurance","volume":"23","author":"Sun","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","unstructured":"Zhang, X., Li, F., and Wu, X. (November, January 9). Csg: Critical scenario generation from real traffic accidents. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tuncali, C.E., Fainekos, G., Ito, H., and Kapinski, J. (2018, January 11\u201313). Sim-atav: Simulation-based adversarial testing framework for autonomous vehicles. Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (Part of CPS Week), Porto, Portugal.","DOI":"10.1145\/3178126.3187004"},{"key":"ref_14","unstructured":"Najm, W.G., Toma, S., and Brewer, J. (2013). Depiction of Priority Light-Vehicle Pre-Crash Scenarios for Safety Applications Based on Vehicle-to-Vehicle Communications, National Highway Traffic Safety Administration. Tech Report."},{"key":"ref_15","unstructured":"Anti\u0107, B., \u010cabarkapa, M., \u010cubrani\u0107-Dobrodolac, M., and \u010ci\u010devi\u0107, S. (2023, April 05). The Influence of Aggressive Driving Behavior and Impulsiveness on Traffic Accidents, Available online: https:\/\/rosap.ntl.bts.gov\/view\/dot\/36298."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.trf.2015.06.001","article-title":"Impulsivity and driver behaviors, offences and accident involvement: A systematic review","volume":"38","year":"2016","journal-title":"Transp. Res. Part Traffic Psychol. Behav."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.aap.2012.06.029","article-title":"Driving anger, emotional and instrumental aggressiveness, and impulsiveness in the prediction of aggressive and transgressive driving","volume":"50","author":"Berdoulat","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8655514","DOI":"10.1155\/2022\/8655514","article-title":"Heterogeneous driver modeling and corner scenarios sampling for automated vehicles testing","volume":"2022","author":"Ge","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1115\/1.2766722","article-title":"Autonomous vehicle-target assignment: A game-theoretical formulation","volume":"129","author":"Arslan","year":"2007","journal-title":"J. Dyn. Syst. Meas. Control. Trans. ASME"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1109\/TITS.2022.3227738","article-title":"Modeling human driving behavior through generative adversarial imitation learning","volume":"24","author":"Bhattacharyya","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.ifacol.2018.07.044","article-title":"A novel driver performance model based on machine learning","volume":"51","author":"Aksjonov","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TVT.2019.2960110","article-title":"Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles","volume":"69","author":"Xing","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_23","unstructured":"Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B., Shen, Y., Shen, Y., Schmid, C., and Li, C. (2021, January 8\u201311). Tnt: Target-driven trajectory prediction. Proceedings of the 5th Annual Conference on Robot Learning, London, UK."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tian, W., Wang, S., Wang, Z., Wu, M., Zhou, S., and Bi, X. (2022). Multi-modal vehicle trajectory prediction by collaborative learning of lane orientation, vehicle interaction, and intention. Sensors, 22.","DOI":"10.3390\/s22114295"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., and Alahi, A. (2018, January 18\u201323). Social gan: Socially acceptable trajectories with generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fang, L., Jiang, Q., Shi, J., and Zhou, B. (2020, January 13\u201319). Tpnet: Trajectory proposal network for motion prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00683"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, H., Zhou, J., Pan, J., Hu, J., and Miao, J. (November, January 19). Optimal vehicle path planning using quadratic optimization for baidu apollo open platform. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304787"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deo, N., and Trivedi, M.M. (2018, January 18\u201323). Convolutional social pooling for vehicle trajectory prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00196"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (2016, January 27\u201330). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Krajewski, R., Bock, J., Kloeker, L., and Eckstein, L. (2018, January 4\u20137). The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569552"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"eaat6766","DOI":"10.1126\/science.aat6766","article-title":"Navigating cognition: Spatial codes for human thinking","volume":"362","author":"Bellmund","year":"2018","journal-title":"Science"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deo, N., and Trivedi, M.M. (2018, January 26\u201330). Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500493"},{"key":"ref_33","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., and Shroff, G. (2016). Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2019.03.001","article-title":"A high-bias, low-variance introduction to machine learning for physicists","volume":"810","author":"Mehta","year":"2019","journal-title":"Phys. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1103\/PhysRevE.62.1805","article-title":"Congested traffic states in empirical observations and microscopic simulations","volume":"62","author":"Treiber","year":"2000","journal-title":"Phys. Rev. E"},{"key":"ref_38","unstructured":"Shalev-Shwartz, S., Shammah, S., and Shashua, A. (2017). On a formal model of safe and scalable self-driving cars. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105937","DOI":"10.1016\/j.aap.2020.105937","article-title":"A comparative study of state-of-the-art driving strategies for autonomous vehicles","volume":"150","author":"Zhao","year":"2021","journal-title":"Accid. Prev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0001-4575(00)00019-1","article-title":"Extended time-to-collision measures for road traffic safety assessment","volume":"33","author":"Minderhoud","year":"2001","journal-title":"Accid. Anal. Prev."},{"key":"ref_41","first-page":"24","article-title":"Near miss determination through use of a scale of danger","volume":"384","author":"Hayward","year":"1972","journal-title":"Highw. Res. Rec."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105517","DOI":"10.1016\/j.aap.2020.105517","article-title":"Are collision and crossing course surrogate safety indicators transferable? a probability based approach using extreme value theory","volume":"143","author":"Borsos","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41062-021-00531-y","article-title":"Time headway distributions for two-lane two-way roads: Case study from dakahliya governorate, egypt","volume":"6","author":"Shoaeb","year":"2021","journal-title":"Innov. Infrastruct. Solut."},{"key":"ref_44","unstructured":"Dendorfer, P., Osep, A., and Leal-Taix\u00e9, L. (December, January 30). Goal-gan: Multimodal trajectory prediction based on goal position estimation. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4570\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:31:41Z","timestamp":1760124701000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094570"],"URL":"https:\/\/doi.org\/10.3390\/s23094570","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,8]]}}}