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

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.

Keywords

• Blockchain
• Machine learning
• Autonomous vehicles
• Reinforcement learning
• Internet of vehicles

References

1. 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
2. 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
3. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
4. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99-127. https://doi.org/10.1162/106365602320169811
5. 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
6. Syed, S. (2022). Q-Learning. In Inference and Learning from Data, 1971–2007. Cambridge University Press. https://doi.org/10.1017/9781009218245.022
7. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
8. 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

9. 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
10. Agarap, A. F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. https://doi.org/10.48550/arXiv.1803.08375
11. 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
12. 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
13. 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
14. Harris, P. R. (2004). An overview of online learning. European Business Review, 16(4), 430. https://doi.org/10.1108/09555340410561723
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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.
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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.
51. 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
52. 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.
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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