{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:16:36Z","timestamp":1778271396126,"version":"3.51.4"},"reference-count":246,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1109\/tits.2026.3651004","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:03:32Z","timestamp":1769115812000},"page":"5001-5023","source":"Crossref","is-referenced-by-count":5,"title":["A Survey on the Application of Large Language Models in Scenario-Based Testing of Automated Driving Systems"],"prefix":"10.1109","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7698-3978","authenticated-orcid":false,"given":"Yongqi","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Automotive Engineering, Graz University of Technology, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7085-2959","authenticated-orcid":false,"given":"Ji","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Automotive Engineering, Graz University of Technology, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3502-5416","authenticated-orcid":false,"given":"Dong","family":"Bi","sequence":"additional","affiliation":[{"name":"Institute of Automotive Engineering, Graz University of Technology, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9300-2181","authenticated-orcid":false,"given":"Tomislav","family":"Mihalj","sequence":"additional","affiliation":[{"name":"Institute of Automotive Engineering, Graz University of Technology, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-7992","authenticated-orcid":false,"given":"Jia","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8246-8085","authenticated-orcid":false,"given":"Arno","family":"Eichberger","sequence":"additional","affiliation":[{"name":"Institute of Automotive Engineering, Graz University of Technology, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2016.09.010"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94896-6_16"},{"key":"ref3","volume-title":"ENABLE-S3 Demonstrator Overview: Final Results","year":"2019"},{"key":"ref4","volume-title":"HEADSTART: Harmonised European Solutions for Testing Automated Road Transport","year":"2018"},{"key":"ref5","volume-title":"StreetWise: Scenario-Based Safety Assessment for Automated Driving","year":"2025"},{"key":"ref6","volume-title":"New Assessment\/Test Method for Automated Driving (NATM) Guidelines for Validating Automated Driving System (ADS)\u2013Amendments To ECE\/TRANS\/WP.29\/2022\/58","year":"2023"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3177753"},{"issue":"8","key":"ref8","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"ref9","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. NIPS","author":"Brown"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3744746"},{"key":"ref11","first-page":"1","article-title":"A survey on large language models: Applications, challenges, limitations, and practical usage","volume":"2023","author":"Hadi","year":"2023","journal-title":"Authorea Preprints"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.738"},{"key":"ref13","article-title":"Stop overthinking: A survey on efficient reasoning for large language models","author":"Sui","year":"2025","journal-title":"arXiv:2503.16419"},{"key":"ref14","article-title":"A systematic survey of prompt engineering in large language models: Techniques and applications","author":"Sahoo","year":"2024","journal-title":"arXiv:2402.07927"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2025.101260"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-eacl.33"},{"key":"ref17","article-title":"Privacy issues in large language models: A survey","author":"Neel","year":"2023","journal-title":"arXiv:2312.06717"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3729219"},{"key":"ref19","first-page":"1","article-title":"Scenario based testing of automated driving systems: A literature survey","volume-title":"Proc. FISITA Web Congr.","author":"Nalic"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3170122"},{"key":"ref21","article-title":"A survey on scenario-based testing for automated driving systems in high-fidelity simulation","author":"Zhong","year":"2021","journal-title":"arXiv:2112.00964"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3259322"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2993730"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.4271\/12-03-04-0020"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3125620"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3579642"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/app12073477"},{"key":"ref28","article-title":"Generative transformations and patterns in LLM-native approaches for software verification and falsification","author":"Braberman","year":"2024","journal-title":"arXiv:2404.09384"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3476326"},{"key":"ref30","article-title":"Aligning multimodal LLM with human preference: A survey","author":"Yu","year":"2025","journal-title":"arXiv:2503.14504"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3406372"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s44212-024-00060-w"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2015.164"},{"key":"ref34","volume-title":"Road Vehicles\u2014Safety of the Intended Functionality","year":"2022"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500406"},{"key":"ref36","volume-title":"ASAM OpenSCENARIO DSL 2.1.0\u2014Dynamic Scenario Description","year":"2024"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3144803"},{"key":"ref38","volume-title":"Road Vehicles\u2013Test Scenarios for Automated Driving Systems\u2014Scenario Based Safety Evaluation Framework","year":"2022"},{"key":"ref39","volume-title":"Road Vehicles\u2013test Scenarios for Automated Driving Systems\u2013specification for Operational Design Domain","year":"2023"},{"key":"ref40","article-title":"LLM4Drive: A survey of large language models for autonomous driving","author":"Yang","year":"2023","journal-title":"arXiv:2311.01043"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/tiv.2024.3402136"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/WACVW60836.2024.00106"},{"key":"ref43","article-title":"XLM for autonomous driving systems: A comprehensive review","author":"Fourati","year":"2024","journal-title":"arXiv:2409.10484"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC58415.2024.10919629"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102786"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10192362"},{"key":"ref47","first-page":"11","article-title":"Generation of tests for safety assessment of V2V platooning trucks","volume-title":"Proc. 27th ITS World Congr.","author":"Op den Camp"},{"key":"ref48","article-title":"Common methodology for data-driven scenario-based safety assurance in the HEADSTART project","author":"Wei\u00dfensteiner"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3390\/vehicles7040124"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2025.07.096"},{"issue":"3","key":"ref51","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3390\/vehicles7030100","article-title":"Scenario metrics for the safety assurance framework of automated vehicles: A review of its application","volume":"7","author":"de Gelder","year":"2025","journal-title":"Vehicles"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/IAVVC63304.2024.10786407"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/IAVVC63304.2024.10786405"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02105"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0025"},{"key":"ref56","article-title":"Bench2Drive-R: Turning real world data into reactive closed-loop autonomous driving benchmark by generative model","author":"You","year":"2024","journal-title":"arXiv:2412.09647"},{"key":"ref57","article-title":"DriveTransformer: Unified transformer for scalable end-to-end autonomous driving","author":"Jia","year":"2025","journal-title":"arXiv:2503.07656"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00731"},{"key":"ref59","article-title":"ActiveAD: Planning-oriented active learning for end-to-end autonomous driving","author":"Lu","year":"2024","journal-title":"arXiv:2403.02877"},{"key":"ref60","article-title":"DriveMoE: Mixture-of-experts for vision-language-action model in end-to-end autonomous driving","author":"Yang","year":"2025","journal-title":"arXiv:2505.16278"},{"key":"ref61","article-title":"Raw2Drive: Reinforcement learning with aligned world models for end-to-end autonomous driving (in CARLA v2)","author":"Yang","year":"2025","journal-title":"arXiv:2505.16394"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72995-9_9"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0443"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3298301"},{"key":"ref65","article-title":"AMP: Autoregressive motion prediction revisited with next token prediction for autonomous driving","author":"Jia","year":"2024","journal-title":"arXiv:2403.13331"},{"key":"ref66","first-page":"1434","article-title":"Multi-agent trajectory prediction by combining egocentric and allocentric views","volume-title":"Proc. Conf. Robot Learn.","author":"Jia"},{"key":"ref67","first-page":"910","article-title":"Towards capturing the temporal dynamics for trajectory prediction: A coarse-to-fine approach","volume-title":"Proc. Conf. Robot Learn.","author":"Xiaosong"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3062309"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA55743.2025.11128084"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3508471"},{"key":"ref71","article-title":"What to explain and how? The challenges and future of using large language models to generate explanations for lawyers in autonomous car accidents","volume-title":"Proc. ECAI Workshop Multimodal, Affect., Interact. Explainable AI (MAI-XAI)","volume":"3803","author":"Xu"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA55743.2025.11128380"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/s42154-025-00364-w"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.splurobonlp-1.1"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ELECO60389.2023.10415934"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3756681.3756987"},{"key":"ref77","article-title":"From dashcam videos to driving simulations: Stress testing automated vehicles against rare events","author":"Miao","year":"2024","journal-title":"arXiv:2411.16027"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i14.33593"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2025.9010178"},{"key":"ref80","article-title":"Natural-language-driven simulation benchmark and copilot for efficient production of object interactions in virtual road scenes","author":"Yang","year":"2023","journal-title":"arXiv:2312.04008"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-023-10132-6"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/3650105.3652296"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/IAVVC63304.2024.10786438"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1145\/3660395.3660411"},{"key":"ref85","article-title":"ScenicNL: Generating probabilistic scenario programs from natural language","author":"Elmaaroufi","journal-title":"arXiv:2405.03709"},{"key":"ref86","article-title":"A new approach to AD\/ADAS test scenario generation using open-source intelligence and large language models","author":"Zorin","year":"2024"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01464"},{"key":"ref88","article-title":"LMM-enhanced safety-critical scenario generation for autonomous driving system testing from non-accident traffic videos","author":"Tian","year":"2024","journal-title":"arXiv:2406.10857"},{"key":"ref89","article-title":"Traffic scene generation from natural language description for autonomous vehicles with large language model","author":"Ruan","year":"2024","journal-title":"arXiv:2409.09575"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-91767-7_20"},{"key":"ref91","article-title":"Promptable closed-loop traffic simulation","volume-title":"Proc. 8th Conf. Robot Learn. (CoRL)","author":"Tan"},{"key":"ref92","article-title":"ChatDyn: Language-driven multi-actor dynamics generation in street scenes","author":"Wei","year":"2024","journal-title":"arXiv:2412.08685"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/IROS60139.2025.11247087"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3392930"},{"key":"ref95","article-title":"Exploring critical testing scenarios for decision-making policies: An LLM approach","author":"Xu","year":"2024","journal-title":"arXiv:2412.06684"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3363232"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2025.108077"},{"key":"ref98","article-title":"TARGET: Automated scenario generation from traffic rules for testing autonomous vehicles via validated LLM-guided knowledge extraction","author":"Deng","year":"2023","journal-title":"arXiv:2305.06018"},{"key":"ref99","first-page":"1497","article-title":"LeGEND: A top-down approach to scenario generation of autonomous driving systems assisted by large language models","volume-title":"Proc. 39th IEEE\/ACM Int. Conf. Automated Softw. Eng.","author":"Tang"},{"key":"ref100","first-page":"268","article-title":"SoVAR: Build generalizable scenarios from accident reports for autonomous driving testing","volume-title":"Proc. 39th IEEE\/ACM Int. Conf. Automated Softw. Eng.","author":"Guo"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC57777.2023.10422308"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3578383"},{"key":"ref103","article-title":"ChatBEV: A visual language model that understands BEV maps","author":"Xu","year":"2025","journal-title":"arXiv:2503.13938"},{"key":"ref104","article-title":"Enhancing autonomous vehicle training with language model integration and critical scenario generation","author":"Tian","year":"2024","journal-title":"arXiv:2404.08570"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC60802.2025.11423790"},{"key":"ref106","article-title":"Controllable traffic simulation through LLM-guided hierarchical reasoning and refinement","author":"Liu","year":"2024","journal-title":"arXiv:2409.15135"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/IV55156.2024.10588843"},{"key":"ref108","article-title":"Language conditioned traffic generation","volume-title":"Proc. 7th Annu. Conf. Robot Learn.","author":"Tan"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1007\/s42154-025-00374-8"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1771"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.4271\/2025-01-7146"},{"key":"ref112","first-page":"144","article-title":"Language-guided traffic simulation via scene-level diffusion","volume-title":"Proc. Conf. Robot Learn.","author":"Zhong"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2474"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i10.33130"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801754"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-78392-0_3"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.5220\/0012738500003702"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/IV55156.2024.10588869"},{"key":"ref119","first-page":"7275","article-title":"BEV-TSR: Text-scene retrieval in BEV space for autonomous driving","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Wei"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61041.2025.00759"},{"key":"ref121","article-title":"Enhancing autonomous driving safety analysis with generative AI: A comparative study on automated hazard and risk assessment","author":"Abbaspour","year":"2024","journal-title":"arXiv:2410.23207"},{"key":"ref122","first-page":"172","article-title":"Welcome your new AI teammate: On safety analysis by leashing large language models","volume-title":"Proc. IEEE\/ACM 3rd Int. Conf. AI Eng. - Softw. Eng. AI","author":"Nouri"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/RE59067.2024.00029"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2024.106608"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-40953-0_35"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2025.100622"},{"key":"ref127","volume-title":"Learning a Bi-Directional Driving Data Generator Via Large Multi-Modal Model Tuning","author":"Zhong","year":"2024"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3443494"},{"key":"ref129","article-title":"Trajectory-LLM: A language-based data generator for trajectory prediction in autonomous driving","volume-title":"Proc. The 13th Int. Conf. Learn. Represent.","author":"Yang"},{"key":"ref130","article-title":"ADriver-I: A general world model for autonomous driving","author":"Jia","year":"2023","journal-title":"arXiv:2311.13549"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01428"},{"key":"ref132","first-page":"1","article-title":"CARLA: An open urban driving simulator","volume-title":"Proc. 1st Annu. Conf. Robot Learn.","author":"Dosovitskiy"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3331024"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.3850\/978-981-14-8593-0_5225-cd"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.5220\/0013250900003890"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58920-2_11"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/IV48863.2021.9575425"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.24900\/ijss\/0201115124.2018.0301"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1007\/s42154-024-00313-z"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3350512"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107610"},{"key":"ref144","volume-title":"Road Vehicles\u2014Functional Safety","year":"2018"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/1349269"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref147","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01100"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105171"},{"key":"ref150","volume-title":"ASAM OpenSCENARIO 2.0.0","year":"2022"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160296"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20047-2_21"},{"key":"ref153","article-title":"Guided conditional diffusion for controllable traffic simulation","author":"Zhong","year":"2022","journal-title":"arXiv:2210.17366"},{"key":"ref154","article-title":"BITS: Bi-level imitation for traffic simulation","author":"Xu","year":"2022","journal-title":"arXiv:2208.12403"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"ref156","volume-title":"National Motor Vehicle Crash Causation Survey","year":"2024"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3190471"},{"key":"ref158","first-page":"1","article-title":"NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR Workshop","author":"Caesar"},{"key":"ref159","volume-title":"An Environment for Autonomous Driving Decision-Making","author":"Leurent","year":"2018"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73116-7_1"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.544"},{"key":"ref162","volume-title":"Apollo: An Open Autonomous Driving Platform","year":"2025"},{"key":"ref163","volume-title":"Introduction To AVUnit","year":"2024"},{"key":"ref164","article-title":"Generating safety-critical driving scenarios for the design of the CAV proving-ground: Using domain knowledge, causality, and large language models","author":"Zhao","year":"2024"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1145\/3314221.3314633"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1861"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i8.32951"},{"key":"ref168","volume-title":"CARLA Scenario Runner","year":"2025"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01473"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340696"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00978"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v40i45.41238"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW67362.2025.00364"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2024.104800"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/8242764"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9973138"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.3390\/app10228154"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2025.130923"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3268703"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3414828"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.26599\/JICV.2023.9210023"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/IV55152.2023.10186614"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1109\/MSR59073.2023.00020"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801369"},{"key":"ref185","article-title":"A framework for automated driving system testable cases and scenarios","author":"Thorn","year":"2018"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20080-9_42"},{"key":"ref187","volume-title":"Environment Simulator Minimalistic (Esmini)","author":"Knabe","year":"2023"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.3141\/2083-12"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-022-09788-7"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1613"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1800"},{"key":"ref192","article-title":"The ultimate guide to fine-tuning LLMs from basics to breakthroughs: An exhaustive review of technologies, research, best practices, applied research challenges and opportunities","author":"Parthasarathy","year":"2024","journal-title":"arXiv:2408.13296"},{"key":"ref193","first-page":"3","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. ICLR","author":"Hu"},{"key":"ref194","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lewis"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-024-00175-8"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2020"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02484"},{"key":"ref198","volume-title":"Bakllava: Baking Sota Multimodality Into Language Models","year":"2025"},{"key":"ref199","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"},{"key":"ref200","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023","journal-title":"arXiv:2307.09288"},{"key":"ref201","article-title":"The Llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arXiv:2407.21783"},{"key":"ref202","article-title":"TinyLlama: An open-source small language model","author":"Zhang","year":"2024","journal-title":"arXiv:2401.02385"},{"key":"ref203","article-title":"Code Llama: Open foundation models for code","author":"Roziere","year":"2023","journal-title":"arXiv:2308.12950"},{"key":"ref204","first-page":"19730","article-title":"BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.146"},{"key":"ref206","article-title":"CodeGemma: Open code models based on gemma","author":"Team","year":"2024","journal-title":"arXiv:2406.11409"},{"key":"ref207","article-title":"Gemma: Open models based on Gemini research and technology","author":"Team","year":"2024","journal-title":"arXiv:2403.08295"},{"key":"ref208","article-title":"ChatGLM: A family of large language models from GLM-130B to GLM-4 all tools","author":"Zeng","year":"2024","journal-title":"arXiv:2406.12793"},{"key":"ref209","article-title":"DeepSeek-coder: When the large language model meets programming\u2014The rise of code intelligence","author":"Guo","year":"2024","journal-title":"arXiv:2401.14196"},{"key":"ref210","article-title":"DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning","author":"Guo","year":"2025","journal-title":"arXiv:2501.12948"},{"key":"ref211","article-title":"LLaVA-NeXT-interleave: Tackling multi-image, video, and 3D in large multimodal models","author":"Li","year":"2024","journal-title":"arXiv:2407.07895"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC45102.2020.9294422"},{"key":"ref213","first-page":"183","article-title":"SUMO (simulation of urban mobility)\u2014An open-source traffic simulation","volume-title":"Proc. 4th middle East Symp. Simul. Model.","author":"Krajzewicz"},{"key":"ref214","volume-title":"Solutions for Virtual Test Driving","year":"2023"},{"key":"ref215","article-title":"Isaac gym: High performance GPU-based physics simulation for robot learning","author":"Makoviychuk","year":"2021","journal-title":"arXiv:2108.10470"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569552"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00803"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00554"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794137"},{"key":"ref220","article-title":"INTERACTION dataset: An international, adversarial and cooperative moTION dataset in interactive driving scenarios with semantic maps","author":"Zhan","year":"2019","journal-title":"arXiv:1910.03088"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413827"},{"key":"ref222","article-title":"HoliCity: A city-scale data platform for learning holistic 3D structures","author":"Zhou","year":"2020","journal-title":"arXiv:2008.03286"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19839-7_24"},{"key":"ref224","article-title":"Argoverse 2: Next generation datasets for self-driving perception and forecasting","author":"Wilson","year":"2023","journal-title":"arXiv:2301.00493"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00843"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i5.28253"},{"key":"ref227","volume-title":"Revolutionizing Autonomous Driving: AIGC Scenario Copilot By 51world","year":"2025"},{"key":"ref228","volume-title":"Octopus: Autonomous Driving Cloud Service","year":"2025"},{"key":"ref229","volume-title":"Foretellix Expands Data Automation Toolchain for AI-Powered AV Development With Breakthrough Simulation Capabilities Using Nvidia Omniverse and Cosmos Transfer","author":"Washabaugh","year":"2025"},{"key":"ref230","volume-title":"Supervised GenAI Simulation Platform for AV, ADAS, Drones, and Robotics","year":"2025"},{"key":"ref231","volume-title":"DEONTIC: Bridging the Gap in Compliance, Driving the Future of Mobility","year":"2025"},{"key":"ref232","volume-title":"IAV Products","year":"2025"},{"key":"ref233","volume-title":"Generative AI for Autonomous Driving: Luxoft\u2019s New AI Tool","year":"2023"},{"key":"ref234","volume-title":"Dspace Works With AWS to Drive Innovation in Autonomous Driving","year":"2024"},{"key":"ref235","volume-title":"LLM-Based Configuration of Virtual Testbeds","year":"2025"},{"key":"ref236","article-title":"GAIA-1: A generative world model for autonomous driving","author":"Hu","year":"2023","journal-title":"arXiv:2309.17080"},{"key":"ref237","volume-title":"Introducing Roadgpt: AI-Powered Scenario Generation for Av Testing","author":"Gambi","year":"2025"},{"key":"ref238","volume-title":"Scenario Creation\u2013Applied Intuition","year":"2025"},{"key":"ref239","volume-title":"Waymo Explores Using Google\u2019s Gemini to Train Its Robotaxis","author":"Hawkins","year":"2025"},{"key":"ref240","volume-title":"Parallel Domain Unveils Reactor, a Generative AIbased Synthetic Data Generation Engine","author":"Dey","year":"2025"},{"key":"ref241","volume-title":"Generate Scenarios for Simulations","year":"2025"},{"key":"ref242","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262019200.001.0001"},{"key":"ref243","doi-asserted-by":"publisher","DOI":"10.1145\/3703155"},{"key":"ref244","volume-title":"SUNRISE Project: Accelerating Safe Deployment of CCAM Systems By a Harmonised Safety Assurance Framework","year":"2025"},{"key":"ref245","volume-title":"SYNERGIES Project: European Platform for Development, Training, Virtual Testing and Validation of CCAM Systems","year":"2025"},{"key":"ref246","volume-title":"CERTAIN Project: Scenario-Based Safety Assurance of CCAM and Related HMI in a Dynamically Evolving Transport System","year":"2025"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6979\/11512109\/11361285.pdf?arnumber=11361285","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T19:41:57Z","timestamp":1778269317000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11361285\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":246,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tits.2026.3651004","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]}}}