Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems (2401.06831v1)

Published 12 Jan 2024 in cs.CL and cs.AI

Abstract: This survey paper explores the transformative influence of frontier AI, foundation models, and LLMs in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities. Frontier AI refers to the forefront of AI technology, encompassing the latest advancements, innovations, and experimental techniques in the field, especially AI foundation models and LLMs. Foundation models, like GPT-4, are large, general-purpose AI models that provide a base for a wide range of applications. They are characterized by their versatility and scalability. LLMs are obtained from finetuning foundation models with a specific focus on processing and generating natural language. They excel in tasks like language understanding, text generation, translation, and summarization. By leveraging vast textual data, including traffic reports and social media interactions, LLMs extract critical insights, fostering the evolution of ITS. The survey navigates the dynamic synergy between LLMs and ITS, delving into applications in traffic management, integration into autonomous vehicles, and their role in shaping smart cities. It provides insights into ongoing research, innovations, and emerging trends, aiming to inspire collaboration at the intersection of language, intelligence, and mobility for safer, more efficient, and sustainable transportation systems. The paper further surveys interactions between LLMs and various aspects of ITS, exploring roles in traffic management, facilitating autonomous vehicles, and contributing to smart city development, while addressing challenges brought by frontier AI and foundation models. This paper offers valuable inspiration for future research and innovation in the transformative domain of intelligent transportation.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. M. S. Sheikh, J. Liang, and W. Wang, “A survey of security services, attacks, and applications for vehicular ad hoc networks (VANETs),” Sensors, vol. 19, no. 16, p. 3589, Aug. 2019.
  2. E. Khanapuri, T. Chintalapati, R. Sharma, and R. Gerdes, “Learning-based adversarial agent detection and identification in cyber physical systems applied to autonomous vehicular platoon,” in Proc. IEEE/ACM 5th Int. Workshop Softw. Eng. Smart Cyber-Phys. Syst. (SEsCPS), May 2019, pp. 39–45.
  3. P. K. Singh, S. K. Jha, S. K. Nandi, and S. Nandi, “ML-based approach to detect DDoS attack in V2I communication under SDN architecture,” in Proc. IEEE Reg. 10 Conf., Oct. 2018, pp. 0144–0149.
  4. Y. Yu, L. Guo, Y. Liu, J. Zheng, and Y. Zong, “An efficient SDNbased DDoS attack detection and rapid response platform in vehicular networks,” IEEE Access, vol. 6, pp. 44570–44579, 2018.
  5. K. A. Alheeti, A. Gruebler, and K. McDonald-Maier, “Intelligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks,” Computers, vol. 5, no. 3, p. 16, Jul. 2016.
  6. K. M. A. Alheeti, A. Gruebler, and K. D. McDonald-Maier, “An intrusion detection system against black hole attacks on the communication network of self-driving cars,” in Proc. 6th Int. Conf. Emerg. Security Technol. (EST), Sep. 2015, pp. 86–91.
  7. P. Gu, R. Khatoun, Y. Begriche, and A. Serhrouchni, “Support vector machine (SVM) based Sybil attack detection in vehicular networks,” in IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2017, pp. 1–6.
  8. Y. Fan, X. Xiao, and W. Feng, “An anti-jamming game in VANET platoon with reinforcement learning,” in Proc. IEEE Int. Conf. Consum. Electron. Taiwan (ICCE-TW), May 2018, pp. 1–2.
  9. D. Karagiannis and A. Argyriou, “Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning,” Veh. Commun., vol. 13, pp. 56–63, Jul. 2018.
  10. S. Gyawali and Y. Qian, “Misbehavior detection using machine learning in vehicular communication networks,” in Proc. IEEE Int. Conf. Commun. (ICC), May 2019, pp. 1–6.
  11. A. Sargolzaei, C. D. Crane, A. Abbaspour, and S. Noei, “A machine learning approach for fault detection in vehicular cyber-physical systems,” in Proc. 15th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), Dec. 2016, pp. 636–640.
  12. L. Maglaras, “A novel distributed intrusion detection system for vehicular ad hoc networks,” Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 4, pp. 101–106, 2015.
  13. W. Li, A. Joshi, and T. Finin, “SVM-CASE: An SVM-based context aware security framework for vehicular ad-hoc networks,” in Proc.IEEE 82nd Veh. Technol. Conf. (VTC-Fall), Sep. 2015, pp. 1–5.
  14. M. Kim, I. Jang, S. Choo, J. Koo, and S. Pack, “Collaborative security attack detection in software-defined vehicular networks,” in Proc. 19th Asia-Pac. Netw. Oper. Manage. Symp. (APNOMS), Sep. 2017, pp. 19–24.
  15. T. Zhang and Q. Zhu, “Differentially private collaborative intrusion detection systems for VANETs,” 2020, arXiv:2005.00703.
  16. J. Grover, N. K. Prajapati, V. Laxmi, and M. S. Gaur, “Machine learning approach for multiple misbehavior detection in VANET,” in Advances in Computing and Communications, A. Abraham, J. L. Mauri, J. F. Buford, J. Suzuki, and S. M. Thampi, Eds. Heidelberg, Germany: Springer, 2011, pp. 644–653.
  17. U. Ihsan, R. Malaney, and S. Yan, “Machine learning and location verification in vehicular networks,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC), Aug. 2019, pp. 91–95.
  18. X. Lu, L. Xiao, T. Xu, Y. Zhao, Y. Tang, and W. Zhuang, “Reinforcement learning based PHY authentication for VANETs,” IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3068–3079, Mar. 2020.
  19. X. Lu, X. Wan, L. Xiao, Y. Tang, and W. Zhuang, “Learning-based rogue edge detection in VANETs with ambient radio signals,” in Proc. IEEE Int. Conf. Commun. (ICC), May 2018, pp. 1–6.
  20. H. Sedjelmaci and S. M. Senouci, “An accurate and efficient collaborative intrusion detection framework to secure vehicular networks,” Comput. Electr. Eng., vol. 43, pp. 33–47, Apr. 2015.
  21. W. Wang, M. Min, L. Xiao, Y. Chen, and H. Dai, “Protecting semantic trajectory privacy for VANET with reinforcement learning,” in Proc. IEEE Int. Conf. Commun. (ICC), 2019, pp. 1–5.
  22. A. R. Narayanadoss, T. Truong-Huu, P. M. Mohan, and M. Gurusamy, “Crossfire attack detection using deep learning in software defined ITS networks,” in Proc. IEEE 89th Veh. Technol. Conf. (VTC-Spring), Apr. 2019, pp. 1–6.
  23. G. D Angelo, A. Castiglione, and F. Palmieri, “A cluster-based multidimensional approach for detecting attacks on connected vehicles,” IEEE Internet Things J., vol. 8, no. 16, pp. 12518–12527, Aug. 2020.
  24. A. Uprety, D. B. Rawat, and J. Li, “Privacy preserving misbehavior detection in IoV using federated machine learning,” in Proc. IEEE 18th Annu. Consum. Commun. Netw. Conf. (CCNC), 2021, pp. 1–6.
  25. P. Sharma and H. Liu, “A machine-learning-based data-centric misbehavior detection model for Internet of Vehicles,” IEEE Internet Things J., vol. 8, no. 6, pp. 4991–4999, Mar. 2021.
  26. Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Federated learning for data privacy preservation in vehicular cyber-physical systems,” IEEE Netw., vol. 34, no. 3, pp. 50–56, May/Jun. 2020.
  27. S. Gyawali, Y. Qian, and R. Q. Hu, “Machine learning and reputation based misbehavior detection in vehicular communication networks,” IEEE Trans. Veh. Technol., vol. 69, no. 8, pp. 8871–8885, Aug. 2020.
  28. I. Rasheed, F. Hu, and L. Zhang, “Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN,” Veh. Commun., vol. 26, Dec. 2020, Art. no. 100266.
  29. S. Tariq, S. Lee, and S. S. Woo, “CANTransfer: Transfer learning based intrusion detection on a controller area network using convolutional LSTM network,” in Proc. 35th Annu. ACM Symp. Appl. Comput., 2020, pp. 1048–1055. [Online]. Available: https://doiorg.libproxy1.nus.edu.sg/10.1145/3341105.3373868
  30. G. Loukas, T. Vuong, R. Heartfield, G. Sakellari, Y. Yoon, and D. Gan, “Cloud-based cyber-physical intrusion detection for vehicles using deep learning,” IEEE Access, vol. 6, pp. 3491–3508, 2018.
  31. M. Van Ly, S. Martin, and M. M. Trivedi, “Driver classification and driving style recognition using inertial sensors,” in Proc. IEEE Intell. Vehicles Symp. (IV), Jun. 2013, pp. 1040–1045.
  32. M. V. Martinez, I. D. Campo, J. Echanobe, and K. Basterretxea, “Driving behavior signals and machine learning: A personalized driver assistance system,” in Proc. IEEE 18th Int. Conf. Intell. Transp. Syst., Sep. 2015, pp. 2933–2940.
  33. M. Enev, A. Takakuwa, K. Koscher, and T. Kohno, “Automobile driver fingerprinting,” Proc. Privacy Enhanc. Technol., vol. 2016, no. 1, pp. 34–51, 2016.
  34. B. I. Kwak, J. Woo, and H. K. Kim, “Know your master: Driver profiling-based anti-theft method,” in Proc. 14th Annu. Conf. Privacy Security Trust (PST), 2016, pp. 211–218.
  35. W. Dong, J. Li, R. Yao, C. Li, T. Yuan, and L. Wang, “Characterizing driving styles with deep learning,” 2016, arXiv:1607.03611.
  36. L. Moreira-Matias and H. Farah, “On developing a driver identification methodology using in-vehicle data recorders,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 9, pp. 2387–2396, Sep. 2017.
  37. N. Virojboonkiate, P. Vateekul, and K. Rojviboonchai, “Driver identification using histogram and neural network from acceleration data,” in Proc. IEEE 17th Int. Conf. Commun. Technol. (ICCT), Oct. 2017, pp. 1560–1564.
  38. W. Dong, T. Yuan, K. Yang, C. Li, and S. Zhang, “Autoencoder regularized network for driving style representation learning,” 2017, arXiv:1701.01272.
  39. F. Martinelli, F. Mercaldo, V. Nardone, A. Orlando, and A. Santone, “Who’s driving my car? A machine learning based approach to driver identification,” in Proc. ICISSP, 2018, pp. 367–372.
  40. A. E. Mekki, A. Bouhoute, and I. Berrada, “Improving driver identification for the next-generation of in-vehicle software systems,” IEEE Trans. Veh. Technol., vol. 68, no. 8, pp. 7406–7415, Aug. 2019.
  41. Wenhan Yu, Terence Jie Chua, Jun Zhao. “Multi-Agent Deep Reinforcement Learning for Digital Twin over 6G Wireless Communication in the Metaverse.” IEEE INFOCOM Workshop on PerAI-6G: Pervasive Network Intelligence for 6G Networks, 2023.
  42. F. Martinelli, F. Mercaldo, and A. Santone, “Machine learning for driver detection through CAN bus,” in Proc. IEEE 91st Veh. Technol. Conf. (VTC2020-Spring), 2020, pp. 1–5.
  43. W. Yuan and Y. Tang, “The driver authentication device based on the characteristics of palmprint and palm vein,” in Proc. Int. Conf. Hand Based Biometrics, 2011, pp. 1–5.
  44. M. Roeschlin, C. Vaas, K. B. Rasmussen, and I. Martinovic, “Bionyms: Driver-centric message authentication using biometric measurements,” in Proc. IEEE Veh. Netw. Conf. (VNC), 2018, pp. 1–8.
  45. L. Song, Q. Han, and J. Liu, “Investigate key management and authentication models in VANETs,” in Proc. Int. Conf. Electron. Commun. Control (ICECC), 2011, pp. 1516–1519.
  46. Zheng, Ou, Dongdong Wang, Zijin Wang, and Shengxuan Ding. “ChatGPT Is on the Horizon: Could a Large Language Model Be Suitable for Intelligent Traffic Safety Research and Applications?” ArXiv, (2023). /abs/2303.05382.
  47. Dong, Liang, and Yunhong Liu. “Frontiers of Policy and Governance Research in a Smart City and Artificial Intelligence: An Advanced Review Based on Natural Language Processing.” Frontiers in Sustainable Cities 5, (2023): 1199041.
  48. Musa, Auwal Alhassan, Salim Idris Malami, Fayez Alanazi, Wassef Ounaies, Mohammed Alshammari, and Sadi Ibrahim Haruna. 2023. “Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations ” Sustainability 15, no. 13: 9859. https://doi.org/10.3390/su15139859
  49. Putri, Tsarina D. “Intelligent Transportation Systems (ITS): A Systematic Review Using a Natural Language Processing (NLP) Approach.” Heliyon 7, no. 12 (2021): e08615.
  50. Wang, Yuan, Dongxiang Zhang, Ying Liu, Bo Dai, and Loo H. Lee. “Enhancing Transportation Systems via Deep Learning: A Survey.” Transportation Research Part C: Emerging Technologies 99, (2019): 144-163.
  51. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez , Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” Advances in Neural Information Processing Systems 30, pp. 30-50 (2017).
  52. Yuan, Yang. “On the power of foundation models.” In International Conference on Machine Learning, pp. 40519-40530. PMLR, 2023.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Mohamed R. Shoaib (5 papers)
  2. Heba M. Emara (4 papers)
  3. Jun Zhao (469 papers)
Citations (4)
View on Semantic Scholar