Review
BibTex RIS Cite

Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey

Year 2024, Volume: 8 Issue: 2, 282 - 299, 30.04.2024
https://doi.org/10.31127/tuje.1366248

Abstract

The integration of blockchain and machine learning technologies has the potential to enable the development of more secure, reliable, and efficient autonomous car systems. Blockchain can be used to store, manage, and share the large amounts of data generated by autonomous vehicle various sensors and cameras, ensuring the integrity and security of these data. Machine learning algorithms can be used to analyze and fuse these data in real time, allowing the vehicle to make informed decisions about how to navigate its environment and respond to changing conditions. Thus, the combination of these technologies has the potential to improve the safety, performance, and scalability of autonomous car systems, making them a more applicable and attractive option for consumers and industry stakeholders. In this paper, all relevant technologies, such as machine learning, blockchain and autonomous cars, were explored. Various techniques of machine learning were investigated, including reinforcement learning strategies, the evolution of artificial neural networks and main deep learning algorithms. The main features of the blockchain technology, as well as its different types and consensus mechanisms, were discussed briefly. Autonomous cars, their different types of sensors, potential vulnerabilities, sensor data fusion techniques, and decision-making models were addressed, and main problem domains and trends were underlined. Furthermore, relevant research discussing blockchain for intelligent transportation systems and internet of vehicles was examined. Subsequently, papers related to the integration of blockchain with machine learning for autonomous cars and vehicles were compared and summarized. Finally, the main applications, challenges and future trends of this integration were highlighted.

References

  • Priyadarshini, I. (2019). Introduction to blockchain technology. Cyber security in parallel and distributed computing: concepts, techniques, applications and case studies, 91-107. https://doi.org/10.1002/9781119488330.ch6
  • Yontar, E. (2023). Challenges, threats and advantages of using blockchain technology in the framework of sustainability of the logistics sector. Turkish Journal of Engineering, 7(3), 186-195. https://doi.org/10.31127/tuje.1094375
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
  • Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99-127. https://doi.org/10.1162/106365602320169811
  • Stanley, K. O., D'Ambrosio, D. B., & Gauci, J. (2009). A hypercube-based encoding for evolving large-scale neural networks. Artificial Life, 15(2), 185-212. https://doi.org/10.1162/artl.2009.15.2.15202
  • Syed, S. (2022). Q-Learning. In Inference and Learning from Data, 1971–2007. Cambridge University Press. https://doi.org/10.1017/9781009218245.022
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • Abaimov, S., & Martellini, M. (2022). Understanding machine learning. In Machine Learning for Cyber Agents: Attack and Defence, 15-89. https://doi.org/10.1007/978-3-030-91585-8_2
  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717-727. https://doi.org/10.1016/S0731-7085(99)00272-1
  • Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. https://doi.org/10.48550/arXiv.1803.08375
  • Xu, J., Li, Z., Du, B., Zhang, M., & Liu, J. (2020). Reluplex made more practical: Leaky ReLU. In 2020 IEEE Symposium on Computers and Communications (ISCC), 1-7. https://doi.org/10.1109/ISCC50000.2020.9219587
  • Liu, T., Qiu, T., & Luan, S. (2019). Hyperbolic-tangent-function-based cyclic correlation: Definition and theory. Signal Processing, 164, 206-216. https://doi.org/10.1016/j.sigpro.2019.06.001
  • Ren, P., Xiao, Y., Chang, X., Huang, P. Y., Li, Z., Gupta, B. B., ... & Wang, X. (2021). A survey of deep active learning. ACM Computing Surveys (CSUR), 54(9), 1-40. https://doi.org/10.1145/3472291
  • Harris, P. R. (2004). An overview of online learning. European Business Review, 16(4), 430. https://doi.org/10.1108/09555340410561723
  • Zhang, Y., & Yeung, D. Y. (2012). A convex formulation for learning task relationships in multi-task learning. arXiv preprint arXiv:1203.3536. https://doi.org/10.48550/arXiv.1203.3536
  • Miller, T., Howe, P., & Sonenberg, L. (2017). Explainable AI: Beware of inmates running the asylum or: How I learnt to stop worrying and love the social and behavioural sciences. arXiv preprint arXiv:1712.00547. https://doi.org/10.48550/arXiv.1712.00547
  • Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3), 50-60. https://doi.org/10.1109/MSP.2020.2975749
  • Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191
  • Do, T. D., Duong, M. T., Dang, Q. V., & Le, M. H. (2018). Real-time self-driving car navigation using deep neural network. In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), 7-12. https://doi.org/10.1109/GTSD.2018.8595590
  • Kouris, A., Venieris, S. I., Rizakis, M., & Bouganis, C. S. (2020). Approximate LSTMs for time-constrained inference: Enabling fast reaction in self-driving cars. IEEE Consumer Electronics Magazine, 9(4), 11-26. https://doi.org/10.1109/MCE.2020.2969195
  • Singh, D., & Srivastava, R. (2022). Graph Neural Network with RNNs based trajectory prediction of dynamic agents for autonomous vehicle. Applied Intelligence, 52(11), 12801-12816. https://doi.org/10.1007/s10489-021-03120-9
  • Zhang, M., Zhang, Y., Zhang, L., Liu, C., & Khurshid, S. (2018). Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, 132-142. https://doi.org/10.1145/3238147.3238187
  • Antonini, P., Ippoliti, G., & Longhi, S. (2006). Learning control of mobile robots using a multiprocessor system. Control Engineering Practice, 14(11), 1279-1295. https://doi.org/10.1016/j.conengprac.2005.06.012
  • Jalali, S. M. J., Ahmadian, S., Khosravi, A., Mirjalili, S., Mahmoudi, M. R., & Nahavandi, S. (2020). Neuroevolution-based autonomous robot navigation: A comparative study. Cognitive Systems Research, 62, 35-43. https://doi.org/10.1016/j.cogsys.2020.04.001
  • Chen, B. W., & Rho, S. (2020). Autonomous tactical deployment of the UAV array using self-organizing swarm intelligence. IEEE Consumer Electronics Magazine, 9(2), 52-56. https://doi.org/10.1109/MCE.2019.2954051
  • Zrira, N., Hannat, M., & Bouyakhf, E. H. (2020). 3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures. Nature-Inspired Computation in Data Mining and Machine Learning, 855, 161-186. https://doi.org/10.1007/978-3-030-28553-1_8
  • Testolin, A., Stoianov, I., Sperduti, A., & Zorzi, M. (2016). Learning orthographic structure with sequential generative neural networks. Cognitive Science, 40(3), 579-606. https://doi.org/10.1111/cogs.12258
  • Zheng, G., Gao, L., Huang, L., & Guan, J. (2021). Ethereum smart contract development in solidity Berlin/Heidelberg, Germany: Springer. https://doi.org/10.1007/978-981-15-6218-1
  • Gursoy, S., Akkus, H. T., & Dogan, M. (2022). The causal relationship between bitcoin energy consumption and cryptocurrency uncertainty. Journal of Business Economics and Finance, 11(1), 58-67. https://doi.org/10.17261/Pressacademia.2022.1552
  • Bach, L. M., Mihaljevic, B., & Zagar, M. (2018). Comparative analysis of blockchain consensus algorithms. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1545-1550. https://doi.org/10.23919/MIPRO.2018.8400278
  • Gervais, A., Karame, G. O., Wüst, K., Glykantzis, V., Ritzdorf, H., & Capkun, S. (2016). On the security and performance of proof of work blockchains. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 3-16. https://doi.org/10.1145/2976749.2978341
  • Saad, S. M. S., & Radzi, R. Z. R. M. (2020). Comparative review of the blockchain consensus algorithm between proof of stake (pos) and delegated proof of stake (dpos). International Journal of Innovative Computing, 10(2), 27-32. https://doi.org/10.11113/ijic.v10n2.272
  • Sousa, J., Bessani, A., & Vukolic, M. (2018). A byzantine fault-tolerant ordering service for the hyperledger fabric blockchain platform. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 51-58. https://doi.org/10.1109/DSN.2018.00018
  • Debeunne, C., & Vivet, D. (2020). A review of visual-LiDAR fusion based simultaneous localization and mapping. Sensors, 20(7), 2068. https://doi.org/10.3390/s20072068
  • Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140. https://doi.org/10.3390/s21062140
  • Cui, G., Zhang, W., Xiao, Y., Yao, L., & Fang, Z. (2022). Cooperative perception technology of autonomous driving in the internet of vehicles environment: A review. Sensors, 22(15), 5535. https://doi.org/10.3390/s22155535
  • Mao, J., Shi, S., Wang, X., & Li, H. (2022). 3D object detection for autonomous driving: A review and new outlooks. arXiv preprint arXiv:2206.09474, 1.
  • Marti, E., De Miguel, M. A., Garcia, F., & Perez, J. (2019). A review of sensor technologies for perception in automated driving. IEEE Intelligent Transportation Systems Magazine, 11(4), 94-108. https://doi.org/10.1109/MITS.2019.2907630
  • Rosique Contreras, M. F., Navarro Lorente, P. J., Fernández Andrés, J. C., & Padilla Urrea, A. M. (2019). A systematic review of perception system and simulators for autonomous vehicles research. Sensors, 19(3), 648. https://doi.org/10.3390/s19030648
  • Kloeden, H., Schwarz, D., Biebl, E. M., & Rasshofer, R. H. (2011). Vehicle localization using cooperative RF-based landmarks. In 2011 IEEE Intelligent Vehicles Symposium (IV), 387-392. https://doi.org/10.1109/IVS.2011.5940474
  • Chen, M., Zhan, X., Tu, J., & Liu, M. (2019). Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties. IEEJ Transactions on Electrical and Electronic Engineering, 14(9), 1348-1358. https://doi.org/10.1002/tee.22936
  • Chen, Q., Ma, X., Tang, S., Guo, J., Yang, Q., & Fu, S. (2019). F-cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 88-100. https://doi.org/10.1145/3318216.3363300
  • Wang, T. H., Manivasagam, S., Liang, M., Yang, B., Zeng, W., & Urtasun, R. (2020). V2vnet: Vehicle-to-vehicle communication for joint perception and prediction. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, 605-621. https://doi.org/10.1007/978-3-030-58536-5_36
  • Xu, R., Xiang, H., Xia, X., Han, X., Li, J., & Ma, J. (2022). Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In 2022 International Conference on Robotics and Automation (ICRA), 2583-2589. https://doi.org/10.1109/ICRA46639.2022.9812038
  • Xu, R., Xiang, H., Tu, Z., Xia, X., Yang, M. H., & Ma, J. (2022). V2x-vit: Vehicle-to-everything cooperative perception with vision transformer. In European Conference on Computer Vision, 107-124. https://doi.org/10.1007/978-3-031-19842-7_7
  • Xu, R., Tu, Z., Xiang, H., Shao, W., Zhou, B., & Ma, J. (2023). CoBEVT: Cooperative bird’s eye view semantic segmentation with sparse transformers. Computer Vision and Pattern Recognition, 205, 989–1000. https://doi.org/10.48550/arXiv.2207.02202
  • Xu, R., Xia, X., Li, J., Li, H., Zhang, S., Tu, Z., ... & Ma, J. (2023). V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13712-13722. https://doi.org/10.1109/CVPR52729.2023.01318
  • Qian, R., Lai, X., & Li, X. (2022). 3D object detection for autonomous driving: A survey. Pattern Recognition, 130, 108796. https://doi.org/10.1016/j.patcog.2022.108796
  • Wulff, F., Schäufele, B., Sawade, O., Becker, D., Henke, B., & Radusch, I. (2018). Early fusion of camera and lidar for robust road detection based on U-Net FCN. In 2018 IEEE Intelligent Vehicles Symposium (IV), 1426-1431. https://doi.org/10.1109/IVS.2018.8500549
  • Ma, Y., Lu, J., Cui, C., Zhao, S., Cao, X., Ye, W., & Wang, Z. (2024). MACP: Efficient Model Adaptation for Cooperative Perception. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 3373-3382.
  • Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, 3354-3361. https://doi.org/10.1109/CVPR.2012.6248074
  • Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., ... & Anguelov, D. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2446-2454.
  • Li, Y., Ma, D., An, Z., Wang, Z., Zhong, Y., Chen, S., & Feng, C. (2022). V2X-Sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving. IEEE Robotics and Automation Letters, 7(4), 10914-10921. https://doi.org/10.1109/LRA.2022.3192802
  • Xu, R., Xiang, H., Xia, X., Han, X., Li, J., & Ma, J. (2022). Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In 2022 International Conference on Robotics and Automation (ICRA), 2583-2589. https://doi.org/10.1109/ICRA46639.2022.9812038
  • Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017). CARLA: An open urban driving simulator. In Conference on robot learning, 1-16. https://doi.org/10.48550/arXiv.1711.03938
  • Ahmad, J., Zia, M. U., Naqvi, I. H., Chattha, J. N., Butt, F. A., Huang, T., & Xiang, W. (2024). Machine learning and blockchain technologies for cybersecurity in connected vehicles. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(1), e1515. https://doi.org/10.1002/widm.1515
  • Sadaf, M., Iqbal, Z., Javed, A. R., Saba, I., Krichen, M., Majeed, S., & Raza, A. (2023). Connected and automated vehicles: Infrastructure, applications, security, critical challenges, and future aspects. Technologies, 11(5), 117. https://doi.org/10.3390/technologies11050117
  • Pelikan, M., Goldberg, D. E., & Lobo, F. G. (2002). A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 21, 5-20. https://doi.org/10.1023/A:1013500812258
  • Claussmann, L., Revilloud, M., Gruyer, D., & Glaser, S. (2019). A review of motion planning for highway autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 21(5), 1826-1848. https://doi.org/10.1109/TITS.2019.2913998
  • Eren, A. & Doğan, H. (2022). Design and implementation of a cost effective vacuum cleaner robot. Turkish Journal of Engineering, 6 (2), 166-177. https://doi.org/10.31127/tuje.830282
  • Ulvi, A. (2020). Importance of unmanned aerial vehicles (UAVs) in the documentation of cultural heritage. Turkish Journal of Engineering, 4 (3), 104-112. https://doi.org/10.31127/tuje.637050
  • Turan, V., Avşar, E., Asadi, D. & Aydın, E. A. (2021). Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations. Turkish Journal of Engineering, 5 (4), 193-200. https://doi.org/10.31127/tuje.744954
  • Rao, Q., & Frtunikj, J. (2018). Deep learning for self-driving cars: Chances and challenges. In Proceedings of the 1st international workshop on software engineering for AI in autonomous systems, 35-38. https://doi.org/10.1145/3194085.3194087
  • Garnett, N., Silberstein, S., Oron, S., Fetaya, E., Verner, U., Ayash, A., ... & Levi, D. (2017). Real-time category-based and general obstacle detection for autonomous driving. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 198-205. https://doi.org/10.1109/ICCVW.2017.32
  • Mallozzi, P., Pelliccione, P., Knauss, A., Berger, C., & Mohammadiha, N. (2019). Autonomous vehicles: state of the art, future trends, and challenges. Automotive Systems and Software Engineering, 347-367. https://doi.org/10.1007/978-3-030-12157-0_16
  • Rehder, T., Koenig, A., Goehl, M., Louis, L., & Schramm, D. (2019). Lane change intention awareness for assisted and automated driving on highways. IEEE Transactions on Intelligent Vehicles, 4(2), 265-276. https://doi.org/10.1109/TIV.2019.2904386
  • Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J. M., ... & Shah, A. (2019). Learning to drive in a day. In 2019 International Conference on Robotics and Automation (ICRA), 8248-8254. https://doi.org/10.1109/ICRA.2019.8793742
  • Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909-4926. https://doi.org/10.1109/TITS.2021.3054625
  • Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., & Cao, W. (2020). A survey on theories and applications for self-driving cars based on deep learning methods. Applied Sciences, 10(8), 2749. https://doi.org/10.3390/app10082749
  • Chen, C., Wu, J., Lin, H., Chen, W., & Zheng, Z. (2019). A secure and efficient blockchain-based data trading approach for internet of vehicles. IEEE Transactions on Vehicular Technology, 68(9), 9110-9121. https://doi.org/10.1109/TVT.2019.2927533
  • Hu, Z., Yang, Y., Wu, J., & Long, C. (2022). A secure and efficient blockchain-based data sharing scheme for location data. In the 2022 4th International Conference on Blockchain Technology, 110-116. https://doi.org/10.1145/3532640.3532655
  • Mikavica, B., & Kostić-Ljubisavljević, A. (2021). Blockchain-based solutions for security, privacy, and trust management in vehicular networks: a survey. The Journal of Supercomputing, 77(9), 9520-9575. https://doi.org/10.1007/s11227-021-03659-x
  • Singh, P. K., Singh, R., Nandi, S. K., Ghafoor, K. Z., Rawat, D. B., & Nandi, S. (2020). Blockchain-based adaptive trust management in internet of vehicles using smart contract. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3616-3630. https://doi.org/10.1109/TITS.2020.3004041
  • Gazdar, T., Alboqomi, O., & Munshi, A. (2022). A decentralized blockchain-based trust management framework for vehicular ad hoc networks. Smart Cities, 5(1), 348-363. https://doi.org/10.3390/smartcities5010020
  • Vattaparambil, S. S., Koduri, R., Nandyala, S., & Manalikandy, M. (2020). Scalable decentralized solution for secure vehicle-to-vehicle communication, 2020-01-0724. https://doi.org/10.4271/2020-01-0724
  • Lin, X., Wu, J., Mumtaz, S., Garg, S., Li, J., & Guizani, M. (2020). Blockchain-based on-demand computing resource trading in IoV-assisted smart city. IEEE Transactions on Emerging Topics in Computing, 9(3), 1373-1385. https://doi.org/10.1109/TETC.2020.2971831
  • Xu, L., Ge, M., & Wu, W. (2022). Edge server deployment scheme of blockchain in IoVs. IEEE Transactions on Reliability, 71(1), 500-509. https://doi.org/10.1109/TR.2022.3142776
  • Cisneros, J. R. A., Fernández-y-Fernández, C. A., & Vázquez, J. J. (2020). Blockchain software system proposal applied to electric self-driving cars charging stations: a TSP academic project. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT), 174-179. https://doi.org/10.1109/CONISOFT50191.2020.00033
  • Mollah, M. B., Zhao, J., Niyato, D., Guan, Y. L., Yuen, C., Sun, S., ... & Koh, L. H. (2020). Blockchain for the internet of vehicles towards intelligent transportation systems: A survey. IEEE Internet of Things Journal, 8(6), 4157-4185. https://doi.org/10.1109/JIOT.2020.3028368
  • Jabbar, R., Dhib, E., Said, A. B., Krichen, M., Fetais, N., Zaidan, E., & Barkaoui, K. (2022). Blockchain technology for intelligent transportation systems: A systematic literature review. IEEE Access, 10, 20995-21031. https://doi.org/10.1109/ACCESS.2022.3149958
  • Gandhi, G. M. (2019). Artificial intelligence integrated blockchain for training autonomous cars. In 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 1, 157-161. https://doi.org/10.1109/ICONSTEM.2019.8918795
  • Agrawal, D., Bansal, R., Fernandez, T. F., & Tyagi, A. K. (2021). Blockchain integrated machine learning for training autonomous cars. In International Conference on Hybrid Intelligent Systems, 27-37. https://doi.org/10.1007/978-3-030-96305-7_4
  • Ahamed, N. N., & Karthikeyan, P. (2020). A reinforcement learning integrated in heuristic search method for self-driving vehicle using blockchain in supply chain management. International Journal of Intelligent Networks, 1, 92-101. https://doi.org/10.1016/j.ijin.2020.09.001
  • Liu, C. H., Lin, Q., & Wen, S. (2018). Blockchain-enabled data collection and sharing for industrial IoT with deep reinforcement learning. IEEE Transactions on Industrial Informatics, 15(6), 3516-3526. https://doi.org/10.1109/TII.2018.2890203
  • Liu, M., Yu, F. R., Teng, Y., Leung, V. C., & Song, M. (2019). Performance optimization for blockchain-enabled industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 15(6), 3559-3570. https://doi.org/10.1109/TII.2019.2897805
  • He, Y., Huang, K., Zhang, G., Yu, F. R., Chen, J., & Li, J. (2021). Bift: A blockchain-based federated learning system for connected and autonomous vehicles. IEEE Internet of Things Journal, 9(14), 12311-12322. https://doi.org/10.1109/JIOT.2021.3135342
  • Jain, S., Ahuja, N. J., Srikanth, P., Bhadane, K. V., Nagaiah, B., Kumar, A., & Konstantinou, C. (2021). Blockchain and autonomous vehicles: Recent advances and future directions. IEEE Access, 9, 130264-130328. https://doi.org/10.1109/ACCESS.2021.3113649
  • Singh, P., Elmi, Z., Lau, Y. Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022). Blockchain and AI technology convergence: Applications in transportation systems. Vehicular Communications, 38, 100521. https://doi.org/10.1016/j.vehcom.2022.100521
Year 2024, Volume: 8 Issue: 2, 282 - 299, 30.04.2024
https://doi.org/10.31127/tuje.1366248

Abstract

References

  • Priyadarshini, I. (2019). Introduction to blockchain technology. Cyber security in parallel and distributed computing: concepts, techniques, applications and case studies, 91-107. https://doi.org/10.1002/9781119488330.ch6
  • Yontar, E. (2023). Challenges, threats and advantages of using blockchain technology in the framework of sustainability of the logistics sector. Turkish Journal of Engineering, 7(3), 186-195. https://doi.org/10.31127/tuje.1094375
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
  • Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99-127. https://doi.org/10.1162/106365602320169811
  • Stanley, K. O., D'Ambrosio, D. B., & Gauci, J. (2009). A hypercube-based encoding for evolving large-scale neural networks. Artificial Life, 15(2), 185-212. https://doi.org/10.1162/artl.2009.15.2.15202
  • Syed, S. (2022). Q-Learning. In Inference and Learning from Data, 1971–2007. Cambridge University Press. https://doi.org/10.1017/9781009218245.022
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • Abaimov, S., & Martellini, M. (2022). Understanding machine learning. In Machine Learning for Cyber Agents: Attack and Defence, 15-89. https://doi.org/10.1007/978-3-030-91585-8_2
  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717-727. https://doi.org/10.1016/S0731-7085(99)00272-1
  • Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. https://doi.org/10.48550/arXiv.1803.08375
  • Xu, J., Li, Z., Du, B., Zhang, M., & Liu, J. (2020). Reluplex made more practical: Leaky ReLU. In 2020 IEEE Symposium on Computers and Communications (ISCC), 1-7. https://doi.org/10.1109/ISCC50000.2020.9219587
  • Liu, T., Qiu, T., & Luan, S. (2019). Hyperbolic-tangent-function-based cyclic correlation: Definition and theory. Signal Processing, 164, 206-216. https://doi.org/10.1016/j.sigpro.2019.06.001
  • Ren, P., Xiao, Y., Chang, X., Huang, P. Y., Li, Z., Gupta, B. B., ... & Wang, X. (2021). A survey of deep active learning. ACM Computing Surveys (CSUR), 54(9), 1-40. https://doi.org/10.1145/3472291
  • Harris, P. R. (2004). An overview of online learning. European Business Review, 16(4), 430. https://doi.org/10.1108/09555340410561723
  • Zhang, Y., & Yeung, D. Y. (2012). A convex formulation for learning task relationships in multi-task learning. arXiv preprint arXiv:1203.3536. https://doi.org/10.48550/arXiv.1203.3536
  • Miller, T., Howe, P., & Sonenberg, L. (2017). Explainable AI: Beware of inmates running the asylum or: How I learnt to stop worrying and love the social and behavioural sciences. arXiv preprint arXiv:1712.00547. https://doi.org/10.48550/arXiv.1712.00547
  • Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3), 50-60. https://doi.org/10.1109/MSP.2020.2975749
  • Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191
  • Do, T. D., Duong, M. T., Dang, Q. V., & Le, M. H. (2018). Real-time self-driving car navigation using deep neural network. In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), 7-12. https://doi.org/10.1109/GTSD.2018.8595590
  • Kouris, A., Venieris, S. I., Rizakis, M., & Bouganis, C. S. (2020). Approximate LSTMs for time-constrained inference: Enabling fast reaction in self-driving cars. IEEE Consumer Electronics Magazine, 9(4), 11-26. https://doi.org/10.1109/MCE.2020.2969195
  • Singh, D., & Srivastava, R. (2022). Graph Neural Network with RNNs based trajectory prediction of dynamic agents for autonomous vehicle. Applied Intelligence, 52(11), 12801-12816. https://doi.org/10.1007/s10489-021-03120-9
  • Zhang, M., Zhang, Y., Zhang, L., Liu, C., & Khurshid, S. (2018). Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, 132-142. https://doi.org/10.1145/3238147.3238187
  • Antonini, P., Ippoliti, G., & Longhi, S. (2006). Learning control of mobile robots using a multiprocessor system. Control Engineering Practice, 14(11), 1279-1295. https://doi.org/10.1016/j.conengprac.2005.06.012
  • Jalali, S. M. J., Ahmadian, S., Khosravi, A., Mirjalili, S., Mahmoudi, M. R., & Nahavandi, S. (2020). Neuroevolution-based autonomous robot navigation: A comparative study. Cognitive Systems Research, 62, 35-43. https://doi.org/10.1016/j.cogsys.2020.04.001
  • Chen, B. W., & Rho, S. (2020). Autonomous tactical deployment of the UAV array using self-organizing swarm intelligence. IEEE Consumer Electronics Magazine, 9(2), 52-56. https://doi.org/10.1109/MCE.2019.2954051
  • Zrira, N., Hannat, M., & Bouyakhf, E. H. (2020). 3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures. Nature-Inspired Computation in Data Mining and Machine Learning, 855, 161-186. https://doi.org/10.1007/978-3-030-28553-1_8
  • Testolin, A., Stoianov, I., Sperduti, A., & Zorzi, M. (2016). Learning orthographic structure with sequential generative neural networks. Cognitive Science, 40(3), 579-606. https://doi.org/10.1111/cogs.12258
  • Zheng, G., Gao, L., Huang, L., & Guan, J. (2021). Ethereum smart contract development in solidity Berlin/Heidelberg, Germany: Springer. https://doi.org/10.1007/978-981-15-6218-1
  • Gursoy, S., Akkus, H. T., & Dogan, M. (2022). The causal relationship between bitcoin energy consumption and cryptocurrency uncertainty. Journal of Business Economics and Finance, 11(1), 58-67. https://doi.org/10.17261/Pressacademia.2022.1552
  • Bach, L. M., Mihaljevic, B., & Zagar, M. (2018). Comparative analysis of blockchain consensus algorithms. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1545-1550. https://doi.org/10.23919/MIPRO.2018.8400278
  • Gervais, A., Karame, G. O., Wüst, K., Glykantzis, V., Ritzdorf, H., & Capkun, S. (2016). On the security and performance of proof of work blockchains. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 3-16. https://doi.org/10.1145/2976749.2978341
  • Saad, S. M. S., & Radzi, R. Z. R. M. (2020). Comparative review of the blockchain consensus algorithm between proof of stake (pos) and delegated proof of stake (dpos). International Journal of Innovative Computing, 10(2), 27-32. https://doi.org/10.11113/ijic.v10n2.272
  • Sousa, J., Bessani, A., & Vukolic, M. (2018). A byzantine fault-tolerant ordering service for the hyperledger fabric blockchain platform. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 51-58. https://doi.org/10.1109/DSN.2018.00018
  • Debeunne, C., & Vivet, D. (2020). A review of visual-LiDAR fusion based simultaneous localization and mapping. Sensors, 20(7), 2068. https://doi.org/10.3390/s20072068
  • Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140. https://doi.org/10.3390/s21062140
  • Cui, G., Zhang, W., Xiao, Y., Yao, L., & Fang, Z. (2022). Cooperative perception technology of autonomous driving in the internet of vehicles environment: A review. Sensors, 22(15), 5535. https://doi.org/10.3390/s22155535
  • Mao, J., Shi, S., Wang, X., & Li, H. (2022). 3D object detection for autonomous driving: A review and new outlooks. arXiv preprint arXiv:2206.09474, 1.
  • Marti, E., De Miguel, M. A., Garcia, F., & Perez, J. (2019). A review of sensor technologies for perception in automated driving. IEEE Intelligent Transportation Systems Magazine, 11(4), 94-108. https://doi.org/10.1109/MITS.2019.2907630
  • Rosique Contreras, M. F., Navarro Lorente, P. J., Fernández Andrés, J. C., & Padilla Urrea, A. M. (2019). A systematic review of perception system and simulators for autonomous vehicles research. Sensors, 19(3), 648. https://doi.org/10.3390/s19030648
  • Kloeden, H., Schwarz, D., Biebl, E. M., & Rasshofer, R. H. (2011). Vehicle localization using cooperative RF-based landmarks. In 2011 IEEE Intelligent Vehicles Symposium (IV), 387-392. https://doi.org/10.1109/IVS.2011.5940474
  • Chen, M., Zhan, X., Tu, J., & Liu, M. (2019). Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties. IEEJ Transactions on Electrical and Electronic Engineering, 14(9), 1348-1358. https://doi.org/10.1002/tee.22936
  • Chen, Q., Ma, X., Tang, S., Guo, J., Yang, Q., & Fu, S. (2019). F-cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 88-100. https://doi.org/10.1145/3318216.3363300
  • Wang, T. H., Manivasagam, S., Liang, M., Yang, B., Zeng, W., & Urtasun, R. (2020). V2vnet: Vehicle-to-vehicle communication for joint perception and prediction. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, 605-621. https://doi.org/10.1007/978-3-030-58536-5_36
  • Xu, R., Xiang, H., Xia, X., Han, X., Li, J., & Ma, J. (2022). Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In 2022 International Conference on Robotics and Automation (ICRA), 2583-2589. https://doi.org/10.1109/ICRA46639.2022.9812038
  • Xu, R., Xiang, H., Tu, Z., Xia, X., Yang, M. H., & Ma, J. (2022). V2x-vit: Vehicle-to-everything cooperative perception with vision transformer. In European Conference on Computer Vision, 107-124. https://doi.org/10.1007/978-3-031-19842-7_7
  • Xu, R., Tu, Z., Xiang, H., Shao, W., Zhou, B., & Ma, J. (2023). CoBEVT: Cooperative bird’s eye view semantic segmentation with sparse transformers. Computer Vision and Pattern Recognition, 205, 989–1000. https://doi.org/10.48550/arXiv.2207.02202
  • Xu, R., Xia, X., Li, J., Li, H., Zhang, S., Tu, Z., ... & Ma, J. (2023). V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13712-13722. https://doi.org/10.1109/CVPR52729.2023.01318
  • Qian, R., Lai, X., & Li, X. (2022). 3D object detection for autonomous driving: A survey. Pattern Recognition, 130, 108796. https://doi.org/10.1016/j.patcog.2022.108796
  • Wulff, F., Schäufele, B., Sawade, O., Becker, D., Henke, B., & Radusch, I. (2018). Early fusion of camera and lidar for robust road detection based on U-Net FCN. In 2018 IEEE Intelligent Vehicles Symposium (IV), 1426-1431. https://doi.org/10.1109/IVS.2018.8500549
  • Ma, Y., Lu, J., Cui, C., Zhao, S., Cao, X., Ye, W., & Wang, Z. (2024). MACP: Efficient Model Adaptation for Cooperative Perception. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 3373-3382.
  • Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, 3354-3361. https://doi.org/10.1109/CVPR.2012.6248074
  • Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., ... & Anguelov, D. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2446-2454.
  • Li, Y., Ma, D., An, Z., Wang, Z., Zhong, Y., Chen, S., & Feng, C. (2022). V2X-Sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving. IEEE Robotics and Automation Letters, 7(4), 10914-10921. https://doi.org/10.1109/LRA.2022.3192802
  • Xu, R., Xiang, H., Xia, X., Han, X., Li, J., & Ma, J. (2022). Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In 2022 International Conference on Robotics and Automation (ICRA), 2583-2589. https://doi.org/10.1109/ICRA46639.2022.9812038
  • Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017). CARLA: An open urban driving simulator. In Conference on robot learning, 1-16. https://doi.org/10.48550/arXiv.1711.03938
  • Ahmad, J., Zia, M. U., Naqvi, I. H., Chattha, J. N., Butt, F. A., Huang, T., & Xiang, W. (2024). Machine learning and blockchain technologies for cybersecurity in connected vehicles. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(1), e1515. https://doi.org/10.1002/widm.1515
  • Sadaf, M., Iqbal, Z., Javed, A. R., Saba, I., Krichen, M., Majeed, S., & Raza, A. (2023). Connected and automated vehicles: Infrastructure, applications, security, critical challenges, and future aspects. Technologies, 11(5), 117. https://doi.org/10.3390/technologies11050117
  • Pelikan, M., Goldberg, D. E., & Lobo, F. G. (2002). A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 21, 5-20. https://doi.org/10.1023/A:1013500812258
  • Claussmann, L., Revilloud, M., Gruyer, D., & Glaser, S. (2019). A review of motion planning for highway autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 21(5), 1826-1848. https://doi.org/10.1109/TITS.2019.2913998
  • Eren, A. & Doğan, H. (2022). Design and implementation of a cost effective vacuum cleaner robot. Turkish Journal of Engineering, 6 (2), 166-177. https://doi.org/10.31127/tuje.830282
  • Ulvi, A. (2020). Importance of unmanned aerial vehicles (UAVs) in the documentation of cultural heritage. Turkish Journal of Engineering, 4 (3), 104-112. https://doi.org/10.31127/tuje.637050
  • Turan, V., Avşar, E., Asadi, D. & Aydın, E. A. (2021). Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations. Turkish Journal of Engineering, 5 (4), 193-200. https://doi.org/10.31127/tuje.744954
  • Rao, Q., & Frtunikj, J. (2018). Deep learning for self-driving cars: Chances and challenges. In Proceedings of the 1st international workshop on software engineering for AI in autonomous systems, 35-38. https://doi.org/10.1145/3194085.3194087
  • Garnett, N., Silberstein, S., Oron, S., Fetaya, E., Verner, U., Ayash, A., ... & Levi, D. (2017). Real-time category-based and general obstacle detection for autonomous driving. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 198-205. https://doi.org/10.1109/ICCVW.2017.32
  • Mallozzi, P., Pelliccione, P., Knauss, A., Berger, C., & Mohammadiha, N. (2019). Autonomous vehicles: state of the art, future trends, and challenges. Automotive Systems and Software Engineering, 347-367. https://doi.org/10.1007/978-3-030-12157-0_16
  • Rehder, T., Koenig, A., Goehl, M., Louis, L., & Schramm, D. (2019). Lane change intention awareness for assisted and automated driving on highways. IEEE Transactions on Intelligent Vehicles, 4(2), 265-276. https://doi.org/10.1109/TIV.2019.2904386
  • Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J. M., ... & Shah, A. (2019). Learning to drive in a day. In 2019 International Conference on Robotics and Automation (ICRA), 8248-8254. https://doi.org/10.1109/ICRA.2019.8793742
  • Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909-4926. https://doi.org/10.1109/TITS.2021.3054625
  • Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., & Cao, W. (2020). A survey on theories and applications for self-driving cars based on deep learning methods. Applied Sciences, 10(8), 2749. https://doi.org/10.3390/app10082749
  • Chen, C., Wu, J., Lin, H., Chen, W., & Zheng, Z. (2019). A secure and efficient blockchain-based data trading approach for internet of vehicles. IEEE Transactions on Vehicular Technology, 68(9), 9110-9121. https://doi.org/10.1109/TVT.2019.2927533
  • Hu, Z., Yang, Y., Wu, J., & Long, C. (2022). A secure and efficient blockchain-based data sharing scheme for location data. In the 2022 4th International Conference on Blockchain Technology, 110-116. https://doi.org/10.1145/3532640.3532655
  • Mikavica, B., & Kostić-Ljubisavljević, A. (2021). Blockchain-based solutions for security, privacy, and trust management in vehicular networks: a survey. The Journal of Supercomputing, 77(9), 9520-9575. https://doi.org/10.1007/s11227-021-03659-x
  • Singh, P. K., Singh, R., Nandi, S. K., Ghafoor, K. Z., Rawat, D. B., & Nandi, S. (2020). Blockchain-based adaptive trust management in internet of vehicles using smart contract. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3616-3630. https://doi.org/10.1109/TITS.2020.3004041
  • Gazdar, T., Alboqomi, O., & Munshi, A. (2022). A decentralized blockchain-based trust management framework for vehicular ad hoc networks. Smart Cities, 5(1), 348-363. https://doi.org/10.3390/smartcities5010020
  • Vattaparambil, S. S., Koduri, R., Nandyala, S., & Manalikandy, M. (2020). Scalable decentralized solution for secure vehicle-to-vehicle communication, 2020-01-0724. https://doi.org/10.4271/2020-01-0724
  • Lin, X., Wu, J., Mumtaz, S., Garg, S., Li, J., & Guizani, M. (2020). Blockchain-based on-demand computing resource trading in IoV-assisted smart city. IEEE Transactions on Emerging Topics in Computing, 9(3), 1373-1385. https://doi.org/10.1109/TETC.2020.2971831
  • Xu, L., Ge, M., & Wu, W. (2022). Edge server deployment scheme of blockchain in IoVs. IEEE Transactions on Reliability, 71(1), 500-509. https://doi.org/10.1109/TR.2022.3142776
  • Cisneros, J. R. A., Fernández-y-Fernández, C. A., & Vázquez, J. J. (2020). Blockchain software system proposal applied to electric self-driving cars charging stations: a TSP academic project. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT), 174-179. https://doi.org/10.1109/CONISOFT50191.2020.00033
  • Mollah, M. B., Zhao, J., Niyato, D., Guan, Y. L., Yuen, C., Sun, S., ... & Koh, L. H. (2020). Blockchain for the internet of vehicles towards intelligent transportation systems: A survey. IEEE Internet of Things Journal, 8(6), 4157-4185. https://doi.org/10.1109/JIOT.2020.3028368
  • Jabbar, R., Dhib, E., Said, A. B., Krichen, M., Fetais, N., Zaidan, E., & Barkaoui, K. (2022). Blockchain technology for intelligent transportation systems: A systematic literature review. IEEE Access, 10, 20995-21031. https://doi.org/10.1109/ACCESS.2022.3149958
  • Gandhi, G. M. (2019). Artificial intelligence integrated blockchain for training autonomous cars. In 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 1, 157-161. https://doi.org/10.1109/ICONSTEM.2019.8918795
  • Agrawal, D., Bansal, R., Fernandez, T. F., & Tyagi, A. K. (2021). Blockchain integrated machine learning for training autonomous cars. In International Conference on Hybrid Intelligent Systems, 27-37. https://doi.org/10.1007/978-3-030-96305-7_4
  • Ahamed, N. N., & Karthikeyan, P. (2020). A reinforcement learning integrated in heuristic search method for self-driving vehicle using blockchain in supply chain management. International Journal of Intelligent Networks, 1, 92-101. https://doi.org/10.1016/j.ijin.2020.09.001
  • Liu, C. H., Lin, Q., & Wen, S. (2018). Blockchain-enabled data collection and sharing for industrial IoT with deep reinforcement learning. IEEE Transactions on Industrial Informatics, 15(6), 3516-3526. https://doi.org/10.1109/TII.2018.2890203
  • Liu, M., Yu, F. R., Teng, Y., Leung, V. C., & Song, M. (2019). Performance optimization for blockchain-enabled industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 15(6), 3559-3570. https://doi.org/10.1109/TII.2019.2897805
  • He, Y., Huang, K., Zhang, G., Yu, F. R., Chen, J., & Li, J. (2021). Bift: A blockchain-based federated learning system for connected and autonomous vehicles. IEEE Internet of Things Journal, 9(14), 12311-12322. https://doi.org/10.1109/JIOT.2021.3135342
  • Jain, S., Ahuja, N. J., Srikanth, P., Bhadane, K. V., Nagaiah, B., Kumar, A., & Konstantinou, C. (2021). Blockchain and autonomous vehicles: Recent advances and future directions. IEEE Access, 9, 130264-130328. https://doi.org/10.1109/ACCESS.2021.3113649
  • Singh, P., Elmi, Z., Lau, Y. Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022). Blockchain and AI technology convergence: Applications in transportation systems. Vehicular Communications, 38, 100521. https://doi.org/10.1016/j.vehcom.2022.100521
There are 88 citations in total.

Details

Primary Language English
Subjects Network Engineering, Communications Engineering (Other)
Journal Section Articles
Authors

Hussam Alkashto 0009-0009-6770-0160

Abdullah Elewi 0000-0001-9774-5292

Early Pub Date April 9, 2024
Publication Date April 30, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Alkashto, H., & Elewi, A. (2024). Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. Turkish Journal of Engineering, 8(2), 282-299. https://doi.org/10.31127/tuje.1366248
AMA Alkashto H, Elewi A. Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. TUJE. April 2024;8(2):282-299. doi:10.31127/tuje.1366248
Chicago Alkashto, Hussam, and Abdullah Elewi. “Integration of Blockchain and Machine Learning for Safe and Efficient Autonomous Car Systems: A Survey”. Turkish Journal of Engineering 8, no. 2 (April 2024): 282-99. https://doi.org/10.31127/tuje.1366248.
EndNote Alkashto H, Elewi A (April 1, 2024) Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. Turkish Journal of Engineering 8 2 282–299.
IEEE H. Alkashto and A. Elewi, “Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey”, TUJE, vol. 8, no. 2, pp. 282–299, 2024, doi: 10.31127/tuje.1366248.
ISNAD Alkashto, Hussam - Elewi, Abdullah. “Integration of Blockchain and Machine Learning for Safe and Efficient Autonomous Car Systems: A Survey”. Turkish Journal of Engineering 8/2 (April 2024), 282-299. https://doi.org/10.31127/tuje.1366248.
JAMA Alkashto H, Elewi A. Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. TUJE. 2024;8:282–299.
MLA Alkashto, Hussam and Abdullah Elewi. “Integration of Blockchain and Machine Learning for Safe and Efficient Autonomous Car Systems: A Survey”. Turkish Journal of Engineering, vol. 8, no. 2, 2024, pp. 282-99, doi:10.31127/tuje.1366248.
Vancouver Alkashto H, Elewi A. Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. TUJE. 2024;8(2):282-99.
Flag Counter