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Review of Autonomous Driving Technology in Intelligent Transportation Systems
- 1 Kings College London
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Abstract
The main problems in transportation are traffic accidents, increasingly slow traffic flow, and pollution. It requires huge infrastructure investments in traditional transportation systems to solve. The advent of autonomous driving techniques with intelligent transportation systems (ITS) can overcome these problems. This paper investigates the integration of autonomous driving technology with intelligent transportation systems (ITS) and explores the latest case studies and research findings on this integration. The purpose is to emphasize the crucial role of merging autonomous driving technology with ITS in-boosting transportation efficiency, ensuring road safety, and fostering sustainability. The study delves into innovations such as intelligent traffic management systems and autonomous logistics distribution vehicles. Key findings highlight the potential of this integration to enhance traffic safety, and efficiency, and reduce congestion and accidents. However, unresolved challenges persist in system integration, data correlation, and hazard detection. The paper concludes by emphasizing the transformative potential of autonomous driving within ITS while proposing future research directions to address these challenges.
Keywords
Autonomous Driving Technology, Intelligent Transportation Systems
[1]. Qureshi, K. N., & Abdullah, A. H. (2013). A survey on intelligent transportation systems. Middle-East Journal of Scientific Research, 15(5), 629-642.
[2]. Dimitrakopoulos, G., & Demestichas, P. (2010). Intelligent transportation systems. IEEE Vehicular Technology Magazine, 5(1), 77-84.
[3]. Rosenzweig, J., & Bartl, M. (2015). A review and analysis of literature on autonomous driving. E-Journal Making-of Innovation, 1-57.
[4]. Alam, M., Ferreira, J., & Fonseca, J. (2016). Introduction to intelligent transportation systems. Intelligent transportation systems: Dependable vehicular communications for improved road safety, 1-17.
[5]. Pütz, F., Murphy, F., & Mullins, M. (2019). Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk. Environment systems and decisions, 39, 383-395.
[6]. Janai, J., Güney, F., Behl, A., & Geiger, A. (2020). Computer vision for autonomous vehicles: Problems, datasets and state of the art. Foundations and Trends® in Computer Graphics and Vision, 12(1–3), 1-308.
[7]. Okereke, C. E., Mohamad, M. M., Wahab, N. H. A., Elijah, O., & Al-Nahari, A. (2023). An overview of machine learning techniques in local path planning for autonomous underwater vehicles. IEEE Access, 11, 24894-24907.
[8]. Arnott, R., & Rowse, J. (2009). Downtown parking in auto city. Regional Science and Urban Economics, 39(1), 1-14.
[9]. Bartl, M. (2015). The future of autonomous driving–introducing the foresight matrix to support strategic planning. The making of Innovation (E Journal), 1-7.
[10]. Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020). A survey of deep learning techniques for autonomous driving. Journal of field robotics, 37(3), 362-386.
[11]. Zhou, Z., Akhtar, Z., Man, K. L., & Siddique, K. (2019). A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems. International journal of distributed sensor networks, 15(11), 1550147719883133.
[12]. Figueiredo, L., Jesus, I., Machado, J. T., Ferreira, J. R., & De Carvalho, J. M. (2001, August). Towards the development of intelligent transportation systems. In ITSC 2001. 2001 IEEE intelligent transportation systems. Proceedings (Cat. No. 01TH8585) (pp. 1206-1211). IEEE.
[13]. Raza, N., Jabbar, S., Han, J., & Han, K. (2018, June). Social vehicle-to-everything (V2X) communication model for intelligent transportation systems based on 5G scenario. In Proceedings of the 2nd international conference on future networks and distributed systems (pp. 1-8).
[14]. I. Meneguette, R., E. De Grande, R., AF Loureiro, A., I. Meneguette, R., E. De Grande, R., & AF Loureiro, A. (2018). Intelligent transportation systems. Intelligent transport system in smart cities: Aspects and challenges of vehicular networks and cloud, 1-21.
[15]. De Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., & Villas, L. A. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4), 1550147716683612.
[16]. Papageorgiou, M., Ben-Akiva, M., Bottom, J., Bovy, P. H., Hoogendoorn, S. P., Hounsell, N. B., ... & McDonald, M. (2007). ITS and traffic management. Handbooks in operations research and management science, 14, 715-774.
[17]. Djahel, S., Doolan, R., Muntean, G. M., & Murphy, J. (2014). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials, 17(1), 125-151.
[18]. Shahgholian, M., & Gharavian, D. (2018). Advanced traffic management systems: an overview and a development strategy. arxiv preprint arxiv:1810.02530.
[19]. Namatherdhala, B., Mazher, N., & Sriram, G. K. (2022). Uses of artificial intelligence in autonomous driving and V2X communication. International Research Journal of Modernization in Engineering Technology and Science, 4(7), 1932-1936.
[20]. Ferreira, B., & Reis, J. (2023). A systematic literature review on the application of automation in logistics. Logistics, 7(4), 80.
[21]. Flechsig, C., Anslinger, F., & Lasch, R. (2022). Robotic Process Automation in purchasing and supply management: A multiple case study on potentials, barriers, and implementation. Journal of Purchasing and Supply Management, 28(1), 100718.
[22]. Benzidia, S., Ageron, B., Bentahar, O., & Husson, J. (2019). Investigating automation and AGV in healthcare logistics: a case study based approach. International Journal of Logistics Research and Applications, 22(3), 273-293.
[23]. Perumal, P. S., Sujasree, M., Chavhan, S., Gupta, D., Mukthineni, V., Shimgekar, S. R., ... & Fortino, G. (2021). An insight into crash avoidance and overtaking advice systems for autonomous vehicles: A review, challenges and solutions. Engineering applications of artificial intelligence, 104, 104406.
Cite this article
Yang,H. (2025). Review of Autonomous Driving Technology in Intelligent Transportation Systems. Theoretical and Natural Science,83,20-26.
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Volume title: Proceedings of the 4th International Conference on Computing Innovation and Applied Physics
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