{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:02:33Z","timestamp":1786978953826,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031656675","type":"print"},{"value":"9783031656682","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-65668-2_20","type":"book-chapter","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T02:02:49Z","timestamp":1721786569000},"page":"295-309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Advancing ITS Applications with\u00a0LLMs: A Survey on\u00a0Traffic Management, Transportation Safety, and\u00a0Autonomous Driving"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5830-3520","authenticated-orcid":false,"given":"Dingkai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6261-6702","authenticated-orcid":false,"given":"Huanran","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6079-991X","authenticated-orcid":false,"given":"Wenjing","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4594-6946","authenticated-orcid":false,"given":"Xiaoling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"20_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"20_CR2","unstructured":"Anil, R., et\u00a0al.: PaLM 2 technical report. arXiv preprint arXiv:2305.10403 (2023)"},{"key":"20_CR3","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877\u20131901 (2020)"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Cao, X., et al.: MAPLM: a real-world large-scale vision-language dataset for map and traffic scene understanding (2023). https:\/\/github.com\/LLVM-AD\/MAPLM","DOI":"10.1109\/CVPR52733.2024.02061"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Cui, C., Ma, Y., Cao, X., Ye, W., Wang, Z.: Drive as you speak: enabling human-like interaction with large language models in autonomous vehicles. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 902\u2013909 (2024)","DOI":"10.1109\/WACVW60836.2024.00101"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Cui, C., Ma, Y., Cao, X., Ye, W., Wang, Z.: Receive, reason, and react: drive as you say, with large language models in autonomous vehicles. IEEE Intell. Transp. Syst. Mag. (2024)","DOI":"10.1109\/MITS.2024.3381793"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Cui, C., et\u00a0al.: A survey on multimodal large language models for autonomous driving. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 958\u2013979 (2024)","DOI":"10.1109\/WACVW60836.2024.00106"},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1109\/TIV.2023.3327715","volume":"9","author":"Y Cui","year":"2024","unstructured":"Cui, Y., et al.: DriveLLM: charting the path toward full autonomous driving with large language models. IEEE Trans. Intell. Veh. 9, 1450\u20131464 (2024)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Da, L., Gao, M., Mei, H., Wei, H.: Prompt to transfer: sim-to-real transfer for traffic signal control with prompt learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 82\u201390 (2024)","DOI":"10.1609\/aaai.v38i1.27758"},{"key":"20_CR11","unstructured":"Devunuri, S., Qiam, S., Lehe, L.: ChatGPT for GTFS: benchmarking LLMs on GTFS understanding and retrieval"},{"key":"20_CR12","unstructured":"Dewangan, V., et al.: Talk2BEV: language-enhanced bird\u2019s-eye view maps for autonomous driving. arXiv preprint arXiv:2310.02251 (2023)"},{"key":"20_CR13","unstructured":"Ding, X., Han, J., Xu, H., Zhang, W., Li, X.: HiLM-D: towards high-resolution understanding in multimodal large language models for autonomous driving. arXiv preprint arXiv:2309.05186 (2023)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Ettinger, S., et al.: Large scale interactive motion forecasting for autonomous driving: the Waymo open motion dataset. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9710\u20139719, October 2021","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Gokul, A.: LLMS and AI: understanding its reach and impact (2023)","DOI":"10.20944\/preprints202305.0195.v1"},{"key":"20_CR16","unstructured":"Hardy, M., Sucholutsky, I., Thompson, B., Griffiths, T.: Large language models meet cognitive science: LLMS as tools, models, and participants. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol.\u00a045 (2023)"},{"key":"20_CR17","unstructured":"Huang, W., Wang, C., Zhang, R., Li, Y., Wu, J., Fei-Fei, L.: VoxPoser: composable 3d value maps for robotic manipulation with language models. arXiv preprint arXiv:2307.05973 (2023)"},{"key":"20_CR18","unstructured":"Lai, S., Xu, Z., Zhang, W., Liu, H., Xiong, H.: Large language models as traffic signal control agents: capacity and opportunity (2023)"},{"key":"20_CR19","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023)"},{"key":"20_CR20","doi-asserted-by":"publisher","first-page":"3080","DOI":"10.1109\/TNSE.2022.3140529","volume":"9","author":"RW Liu","year":"2022","unstructured":"Liu, R.W., Liang, M., Nie, J., Lim, W.Y.B., Zhang, Y., Guizani, M.: Deep learning-powered vessel trajectory prediction for improving smart traffic services in maritime internet of things. IEEE Trans. Netw. Sci. Eng. 9, 3080\u20133094 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Ma, Y., Cao, Y., Sun, J., Pavone, M., Xiao, C.: Dolphins: multimodal language model for driving. arXiv preprint arXiv:2312.00438 (2023)","DOI":"10.1007\/978-3-031-72995-9_23"},{"key":"20_CR22","unstructured":"Mao, J., Qian, Y., Zhao, H., Wang, Y.: GPT-driver: Learning to drive with GPT. arXiv preprint arXiv:2310.01415 (2023)"},{"key":"20_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-031-43458-7_34","volume-title":"The Semantic Web: ESWC 2023 Satellite Events","author":"A Martino","year":"2023","unstructured":"Martino, A., Iannelli, M., Truong, C.: Knowledge injection to counter large language model (LLM) hallucination. In: Pesquita, C., et al. (eds.) ESWC 2023. LNCS, vol. 13998, pp. 182\u2013185. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43458-7_34"},{"key":"20_CR24","unstructured":"Mumtarin, M., Chowdhury, M.S., Wood, J.: Large language models in analyzing crash narratives \u2013 a comparative study of ChatGPT, BARD and GPT-4. arXiv preprint arXiv:2308.13563 (2023)"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Qian, T., Chen, J., Zhuo, L., Jiao, Y., Jiang, Y.G.: NuScenes-QA: a multi-modal visual question answering benchmark for autonomous driving scenario. arXiv preprint arXiv:2305.14836 (2023)","DOI":"10.1609\/aaai.v38i5.28253"},{"key":"20_CR26","unstructured":"Ramesh, A., et al.: Zero-shot text-to-image generation. In: International Conference on Machine Learning, pp. 8821\u20138831. PMLR (2021)"},{"key":"20_CR27","unstructured":"Shah, D., Osinski, B., Ichter, B., Levine, S.: LM-Nav: robotic navigation with large pre-trained models of language, vision, and action. In: Conference on Robot Learning (2022)"},{"key":"20_CR28","unstructured":"Sharan, S., Pittaluga, F., Chandraker, M., et\u00a0al.: LLM-assist: enhancing closed-loop planning with language-based reasoning. arXiv preprint arXiv:2401.00125 (2023)"},{"key":"20_CR29","doi-asserted-by":"publisher","first-page":"1012206","DOI":"10.1155\/2022\/1012206","volume":"2022","author":"MS Sheikh","year":"2022","unstructured":"Sheikh, M.S., Peng, Y., et al.: A comprehensive review on traffic control modeling for obtaining sustainable objectives in a freeway traffic environment. J. Adv. Transp. 2022, 1012206 (2022)","journal-title":"J. Adv. Transp."},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Shoaib, M.R., Emara, H.M., Zhao, J.: A survey on the applications of frontier AI, foundation models, and large language models to intelligent transportation systems. In: 2023 International Conference on Computer and Applications (ICCA), pp.\u00a01\u20137. IEEE (2023)","DOI":"10.1109\/ICCA59364.2023.10401518"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Song, C.H., Wu, J., Washington, C., Sadler, B.M., Chao, W.L., Su, Y.: LLM-planner: few-shot grounded planning for embodied agents with large language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2998\u20133009 (2023)","DOI":"10.1109\/ICCV51070.2023.00280"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Sun, W., Abdullah, L.N., Khalid, F., binti Sulaiman, P.S.: Intelligent analysis of vehicle accidents to detect road safety: a systematic literature review. Int. J. Acad. Res. Bus. Soc. Sci. 13, 1\u201317 (2023)","DOI":"10.6007\/IJARBSS\/v13-i11\/19260"},{"key":"20_CR33","unstructured":"Touvron, H., et\u00a0al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Villarreal, M., Poudel, B., Li, W.: Can ChatGPT enable its? The case of mixed traffic control via reinforcement learning. In: ITSC, pp. 3749\u20133755. IEEE (2023)","DOI":"10.1109\/ITSC57777.2023.10422410"},{"key":"20_CR35","unstructured":"Wang, G., et al.: Voyager: an open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291 (2023)"},{"key":"20_CR36","unstructured":"Wang, L., et al.: AccidentGPT: accident analysis and prevention from V2X environmental perception with multi-modal large model. arXiv preprint arXiv:2312.13156 (2023)"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Wang, T., et al.: DeepAccident: a motion and accident prediction benchmark for V2X autonomous driving. arXiv preprint arXiv:2304.01168 (2023)","DOI":"10.1609\/aaai.v38i6.28370"},{"key":"20_CR38","unstructured":"Wang, W., et\u00a0al.: VisionLLM: large language model is also an open-ended decoder for vision-centric tasks. In: Advances in Neural Information Processing Systems 36 (2024)"},{"key":"20_CR39","unstructured":"Wang, W., et\u00a0al.: DriveMLM: aligning multi-modal large language models with behavioral planning states for autonomous driving. arXiv preprint arXiv:2312.09245 (2023)"},{"key":"20_CR40","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, D., Chen, L., Wang, F.Y., Lin, Y.: Building transportation foundation model via generative graph transformer. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp. 6042\u20136047. IEEE (2023)","DOI":"10.1109\/ITSC57777.2023.10422572"},{"key":"20_CR41","unstructured":"Wen, L., et al.: DiLu: a knowledge-driven approach to autonomous driving with large language models. arXiv preprint arXiv:2309.16292 (2023)"},{"key":"20_CR42","doi-asserted-by":"crossref","unstructured":"Xu, Z., et al.: DriveGPT4: interpretable end-to-end autonomous driving via large language model. arXiv preprint arXiv:2310.01412 (2023)","DOI":"10.1109\/LRA.2024.3440097"},{"key":"20_CR43","unstructured":"Yang, S., et al.: Lidar-LLM: exploring the potential of large language models for 3d lidar understanding. arXiv preprint arXiv:2312.14074 (2023)"},{"key":"20_CR44","unstructured":"Yao, S., et al.: React: synergizing reasoning and acting in language models. In: International Conference on Learning Representations (2023)"},{"key":"20_CR45","doi-asserted-by":"crossref","unstructured":"Yu, H., et al.: DAIR-V2X: a large-scale dataset for vehicle-infrastructure cooperative 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21361\u201321370, June 2022","DOI":"10.1109\/CVPR52688.2022.02067"},{"key":"20_CR46","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Ding, J., Feng, J., Jin, D., Li, Y.: UniST: a prompt-empowered universal model for urban spatio-temporal prediction. arXiv preprint arXiv:2402.11838 (2024)","DOI":"10.1145\/3637528.3671662"},{"issue":"22","key":"20_CR47","doi-asserted-by":"publisher","first-page":"9225","DOI":"10.3390\/s23229225","volume":"23","author":"I de Zarz\u00e0","year":"2023","unstructured":"de Zarz\u00e0, I., de Curt\u00f2, J., Roig, G., Calafate, C.T.: LLM multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023)","journal-title":"Sensors"},{"key":"20_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102038","volume":"102","author":"K Zhang","year":"2024","unstructured":"Zhang, K., Zhou, F., Wu, L., Xie, N., He, Z.: Semantic understanding and prompt engineering for large-scale traffic data imputation. Inf. Fusion 102, 102038 (2024)","journal-title":"Inf. Fusion"},{"key":"20_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: Analysis of traffic accident based on knowledge graph. J. Adv. Transp. (2022)","DOI":"10.1155\/2022\/3915467"},{"key":"20_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: TrafficGPT: viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023)","DOI":"10.1016\/j.tranpol.2024.03.006"},{"key":"20_CR51","unstructured":"Zheng, O., Abdel-Aty, M., Wang, D., Wang, Z., Ding, S.: ChatGPT is on the horizon: could a large language model be all we need for intelligent transportation? arXiv preprint arXiv:2303.05382 (2023)"},{"key":"20_CR52","doi-asserted-by":"crossref","unstructured":"Zheng, W., Chen, W., Huang, Y., Zhang, B., Duan, Y., Lu, J.: OccWorld: learning a 3d occupancy world model for autonomous driving. arXiv preprint arXiv:2311.16038 (2023)","DOI":"10.1007\/978-3-031-72624-8_4"},{"key":"20_CR53","unstructured":"Zhou, X., Knoll, A.C.: GPT-4v as traffic assistant: an in-depth look at vision language model on complex traffic events. arXiv preprint arXiv:2402.02205 (2024)"}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-65668-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T13:21:39Z","timestamp":1732454499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-65668-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031656675","9783031656682"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-65668-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCRS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rough Sets","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Halifax, NS","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ijcrs24.cs.smu.ca","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}