A review of technology evolution and risk of autonomous vehicles
Article Sidebar
Keywords:
Autonomous vehicle, AV, Deep learning, Risk assessment, Technology evolution
Main Article Content
Abstract
The development of autonomous vehicles (AVs) have become a prominent research topic; however, many survey studies focus either on enabling technologies or on isolated risk issues. This approach therefore provides limited insight into how both dimensions of AV development evolve together. The present study employs scientometric analysis of Scopus-indexed journal articles to map the knowledge base of AV technology evolution and risk. The results of the study highlight influential documents and productive countries, identify major research clusters using the log-likelihood ratio (LLR), and reveal thematic shifts across three periods (early, middle, and late). To strengthen the analytical contribution, the revised manuscript synthesizes the interaction between technology phases, dominant methods, associated risks, and corresponding research responses. The findings indicate that early AV studies emphasized autonomy and dynamics. However, there was a subsequent shift towards systems, control, and learning. Moreover, there has been an increasing convergence with risk themes such as cybersecurity, safety assessment, and anomaly detection. These findings of this study offer a more integrated understanding of the co-evolution of AV research and indicate priority challenges for the safe deployment of learning-based methods. Ultimately, the insights provided in this review offer a valuable foundation for policymakers, automotive engineers, and researchers to develop holistic strategies that concurrently address technical innovations and their associated safety or regulatory risks.
Downloads
Download data is not yet available.
Article Details
How to Cite
Nugroho, B., Prasetyadi, A., Trianggoro, C., Rezaldi, M. Y., & Kadir, R. A. (2026). A review of technology evolution and risk of autonomous vehicles. Communications in Science and Technology, 11(1), 107–114. https://doi.org/10.21924/cst.11.1.2026.1888
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
1. S. Kato, E. Takeuchi, Y. Ishiguro, Y. Ninomiya, K. Takeda, and T. Hamada, “An open approach to autonomous vehicles,” IEEE Micro, vol. 35, no. 6, pp. 60–68, 2015.
2. T. Litman, “Autonomous Vehicle Implementation Predictions Implications for Transport Planning,” Victoria Transport Policy Institute, Victoria, Tech. Rep., 2021.
3. M. Aria, “Algoritma perencanaan jalur kendaraan otonom di lingkungan perkotaan dari sudut pandang filosofi kuhn dan filosofi popper,” Telekon-tran, vol. 7, no. 2, pp. 145–150, 2019.
4. R. Lemos, O. Garcia, and J. V. Ferreira, “Local and global path generation for autonomous vehicles using splines,” Ingenier ́ıa, vol. 21, no. 2, pp. 188– 200, 2016.
5. B. Paden, M. Cˇ ap, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of ́motion planning and control techniques for self-driving urban vehicles,” IEEE Trans. Intell. Veh., vol. 1, no. 1, pp. 33–55, 2016.
6. A. Saykin, G. Tuktakiev, A. Zhuravlev, and E. Zaitseva, “The development of ground unmanned vehicles, driver assistance systems and components according to patent publications,” in IOP Conf. Ser.: Mater. Sci. Eng. IOP Publishing, 2018, p. 012025.
7. S. Li, E. Garces, and T. Daim, “Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles,” Technol. Forecast. Soc. Change, vol. 148, p. 119731, 2019.
8. D. Meng, X. Li, Y. Cai, and J. Shi, “Patterns of knowledge development and diffusion in the global autonomous vehicle technological innovation system: a patent-based analysis,” Int. J. Automot. Technol. Manag., vol. 19, no. 1-2, pp. 144–177, 2019.
9. F. Si and L. Guoqiu, “Patent Analyze of Globe Driverless Cars:from the Perspective of Industry Chain and Technology Chain,” Compet. Intell., vol. 12, no. 5, pp. 27–36, 2016.
10. LexInnova, “Cars, Driverless Analysis, Patent Landscape,” pp. 1–24, 2016.
11. K. Bimbraw, “Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology,” in Proc. 12th Int. Conf. Inform. Control Autom. Robot. (ICINCO), vol. 1. IEEE, 2015, pp. 191–198.
12. C. Badue, R. Guidolini, R. V. Carneiro, P. Azevedo, V. B. Cardoso, A. Forechi, L. Jesus, R. Berriel, T. M. Paixao, F. Mutz et al., “Self-driving cars: A survey,” Expert Syst. Appl., vol. 165, p. 113816, 2021.
13. J. Zhao, B. Liang, and Q. Chen, “The key technology toward the self-driving car,” Int. J. Intell. Unmanned Syst., vol. 6, no. 1, pp. 2–20, 2018.
14. C. Chen, F. Ibekwe-SanJuan, and J. Hou, “The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis,” J. Am. Soc. Inf. Sci. Technol., vol. 61, no. 7, pp. 1386–1409, 2010.
15. L. Leydesdorff, “Katy borner: Atlas of Science: Visualizing what we ̈know,” Scientometrics, vol. 88, no. 2, pp. 675–677, Aug. 2011.
16. X. Xiao, M. Skitmore, H. Li, and B. Xia, “Mapping knowledge in the economic areas of green building using scientometric analysis,” Energies, vol. 12, no. 15, p. 3011, 2019.
17. G. Gonzelez-Alcaide, A. Salinas, and J. M. Ramos, “Scientometrics analysis of research activity and collaboration patterns in chagas cardiomyopathy,” PLoS Negl. Trop. Dis., vol. 12, no. 6, p. e0006602, 2018.
18. C. Chen, “Science mapping: a systematic review of the literature,” J. Data Inf. Sci., vol. 2, no. 2, pp. 1–40, 2017.
19. X. Li, P. Wu, G. Q. Shen, X. Wang, and Y. Teng, “Mapping the knowledge domains of building information modeling (bim): A bibliometric approach,” Autom. Constr., vol. 84, pp. 195–206, 2017.
20. M. Aria and C. Cuccurullo, “bibliometrix: An r-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, 2017.
21. L. Bornmann and R. Mutz, “Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references,” J. Assoc. Inf. Sci. Technol., vol. 66, no. 11, pp. 2215–2222, 2015.
22. Y. Fang, J. Yin, and B. Wu, “Climate change and tourism: A scientometric analysis using citespace,” J. Sustain. Tour., vol. 26, no. 1, pp. 108–126, 2018.
23. Q. He, G. Wang, L. Luo, Q. Shi, J. Xie, and X. Meng, “Mapping the managerial areas of building information modeling (bim) using scientometric analysis,” Int. J. Proj. Manag., vol. 35, no. 4, pp. 670–685, 2017.
24. D. Negrut, R. Serban, A. Elmquist, D. Hatch, E. Nutt, and P. Sheets, “Autonomous vehicles in the cyberspace: Accelerating testing via computer simulation,” SAE Technical Paper, Tech. Rep., 2018.
25. K. Shojaei, “Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque,” Ocean Eng., vol. 105, pp. 196–205, 2015.
26. A. A. Pereira, J. Binney, G. A. Hollinger, and G. S. Sukhatme, “Risk-aware path planning for autonomous underwater vehicles using predictive ocean models,” J. Field Robot., vol. 30, no. 5, pp. 741–762, 2013.
27. F. Trapsilawati, T. Wijayanto, and E. Jourdy, “Human-computer trust in navigation systems: google maps vs waze,” Comm. Sci. Tech., vol. 4, no. 1, pp. 38–43, 2019.
28. K. Kim, J. S. Kim, S. Jeong, J.-H. Park, and H. K. Kim, “Cybersecurity for autonomous vehicles: Review of attacks and defense,” Comput. Secur., vol. 103, p. 102150, 2021.
29. A. Khadka, P. Karypidis, A. Lytos, and G. Efstathopoulos, “A benchmarking framework for cyber-attacks on autonomous vehicles,” Transp. Res. Procedia, vol. 52, pp. 323–330, 2021.
30. S. Parkinson, P. Ward, K. Wilson, and J. Miller, “Cyber threats facing autonomous and connected vehicles: Future challenges,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 11, pp. 2898–2915, 2017.
31. L. Hansson, “Regulatory governance in emerging technologies: The case of autonomous vehicles in sweden and norway,” Res. Transp. Econ., vol. 83, p. 100967, 2020.
32. A. Miglani and N. Kumar, “Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges,” Veh. Commun., vol. 20, p. 100184, 2019.
33. J. Hua and A. Faghri, “Applications of artificial neural networks to intelligent vehicle-highway systems,” Transp. Res. Rec., vol. 1453, p. 83, 1994.
34. A. Stathopoulos, L. Dimitriou, and T. Tsekeris, “Fuzzy modeling approach for combined forecasting of urban traffic flow,” Comput.-Aided Civ. Infrastruct. Eng., vol. 23, no. 7, pp. 521–535, 2008.
35. B. L. Smith and M. J. Demetsky, “Traffic flow forecasting: comparison of modeling approaches,” J. Transp. Eng., vol. 123, no. 4, pp. 261–266, 1997.
36. S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” J. Field Robot., vol. 37, no. 3, pp. 362–386, 2020.
37. S. Shalev-Shwartz, S. Shammah, and A. Shashua, “Safe, multi-agent, reinforcement learning for autonomous driving,” arXiv:1610.03295, 2016.
38. S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, D. Rus, and M. H. Ang, “Perception, planning, control, and coordination for autonomous vehicles,” Machines, vol. 5, no. 1, p. 6, 2017.
39. E. Rehder, J. Quehl, and C. Stiller, “Driving like a human: Imitation learning for path planning using convolutional neural networks,” in Proc. Int. Conf. Robot. Autom. Workshops, 2017, pp. 1–5.
40. S. M. Grigorescu, B. Trasnea, L. Marina, A. Vasilcoi, and T. Cocias, “Neurotrajectory: A neuroevolutionary approach to local state trajectory learning for autonomous vehicles,” IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 3441–3448, 2019.
41. L. Sun, C. Peng, W. Zhan, and M. Tomizuka, “A fast integrated planning and control framework for autonomous driving via imitation learning,” in Proc. Dyn. Syst. Control Conf., vol. 51913. American Society of Mechanical Engineers, 2018, p. V003T37A012.
42. L. Yu, X. Shao, Y. Wei, and K. Zhou, “Intelligent land-vehicle model transfer trajectory planning method based on deep reinforcement learning,” Sensors, vol. 18, no. 9, p. 2905, 2018.
43. C. Paxton, V. Raman, G. D. Hager, and M. Kobilarov, “Combining neural networks and tree search for task and motion planning in challenging environments,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS). IEEE, 2017, pp. 6059–6066.
44. M. Naumann, H. Konigshof, M. Lauer, and C. Stiller, “Safe but not overcautious motion planning under occlusions and limited sensor range,” in Proc. IEEE Intell. Veh. Symp. (IV). IEEE, 2019, pp. 140–145.
45. M. Strickland, G. Fainekos, and H. B. Amor, “Deep predictive models for collision risk assessment in autonomous driving,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA). IEEE, 2018, pp. 4685–4692.
46. R. Lee, O. J. Mengshoel, A. Saksena, R. W. Gardner, D. Genin, J. Silbermann, M. Owen, and M. J. Kochenderfer, “Adaptive stress testing: Finding likely failure events with reinforcement learning,” J. Artif. Intell. Res., vol. 69, pp. 1165–1201, 2020.
47. D. Lu, H. Du, Z. Wu, and S. Yang, “Risk assessment in autonomous driving: a comprehensive survey of risk sources, methodologies, and system architectures,” Auton. Intell. Syst., vol. 5, no. 1, p. 24, 2025.
2. T. Litman, “Autonomous Vehicle Implementation Predictions Implications for Transport Planning,” Victoria Transport Policy Institute, Victoria, Tech. Rep., 2021.
3. M. Aria, “Algoritma perencanaan jalur kendaraan otonom di lingkungan perkotaan dari sudut pandang filosofi kuhn dan filosofi popper,” Telekon-tran, vol. 7, no. 2, pp. 145–150, 2019.
4. R. Lemos, O. Garcia, and J. V. Ferreira, “Local and global path generation for autonomous vehicles using splines,” Ingenier ́ıa, vol. 21, no. 2, pp. 188– 200, 2016.
5. B. Paden, M. Cˇ ap, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of ́motion planning and control techniques for self-driving urban vehicles,” IEEE Trans. Intell. Veh., vol. 1, no. 1, pp. 33–55, 2016.
6. A. Saykin, G. Tuktakiev, A. Zhuravlev, and E. Zaitseva, “The development of ground unmanned vehicles, driver assistance systems and components according to patent publications,” in IOP Conf. Ser.: Mater. Sci. Eng. IOP Publishing, 2018, p. 012025.
7. S. Li, E. Garces, and T. Daim, “Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles,” Technol. Forecast. Soc. Change, vol. 148, p. 119731, 2019.
8. D. Meng, X. Li, Y. Cai, and J. Shi, “Patterns of knowledge development and diffusion in the global autonomous vehicle technological innovation system: a patent-based analysis,” Int. J. Automot. Technol. Manag., vol. 19, no. 1-2, pp. 144–177, 2019.
9. F. Si and L. Guoqiu, “Patent Analyze of Globe Driverless Cars:from the Perspective of Industry Chain and Technology Chain,” Compet. Intell., vol. 12, no. 5, pp. 27–36, 2016.
10. LexInnova, “Cars, Driverless Analysis, Patent Landscape,” pp. 1–24, 2016.
11. K. Bimbraw, “Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology,” in Proc. 12th Int. Conf. Inform. Control Autom. Robot. (ICINCO), vol. 1. IEEE, 2015, pp. 191–198.
12. C. Badue, R. Guidolini, R. V. Carneiro, P. Azevedo, V. B. Cardoso, A. Forechi, L. Jesus, R. Berriel, T. M. Paixao, F. Mutz et al., “Self-driving cars: A survey,” Expert Syst. Appl., vol. 165, p. 113816, 2021.
13. J. Zhao, B. Liang, and Q. Chen, “The key technology toward the self-driving car,” Int. J. Intell. Unmanned Syst., vol. 6, no. 1, pp. 2–20, 2018.
14. C. Chen, F. Ibekwe-SanJuan, and J. Hou, “The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis,” J. Am. Soc. Inf. Sci. Technol., vol. 61, no. 7, pp. 1386–1409, 2010.
15. L. Leydesdorff, “Katy borner: Atlas of Science: Visualizing what we ̈know,” Scientometrics, vol. 88, no. 2, pp. 675–677, Aug. 2011.
16. X. Xiao, M. Skitmore, H. Li, and B. Xia, “Mapping knowledge in the economic areas of green building using scientometric analysis,” Energies, vol. 12, no. 15, p. 3011, 2019.
17. G. Gonzelez-Alcaide, A. Salinas, and J. M. Ramos, “Scientometrics analysis of research activity and collaboration patterns in chagas cardiomyopathy,” PLoS Negl. Trop. Dis., vol. 12, no. 6, p. e0006602, 2018.
18. C. Chen, “Science mapping: a systematic review of the literature,” J. Data Inf. Sci., vol. 2, no. 2, pp. 1–40, 2017.
19. X. Li, P. Wu, G. Q. Shen, X. Wang, and Y. Teng, “Mapping the knowledge domains of building information modeling (bim): A bibliometric approach,” Autom. Constr., vol. 84, pp. 195–206, 2017.
20. M. Aria and C. Cuccurullo, “bibliometrix: An r-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, 2017.
21. L. Bornmann and R. Mutz, “Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references,” J. Assoc. Inf. Sci. Technol., vol. 66, no. 11, pp. 2215–2222, 2015.
22. Y. Fang, J. Yin, and B. Wu, “Climate change and tourism: A scientometric analysis using citespace,” J. Sustain. Tour., vol. 26, no. 1, pp. 108–126, 2018.
23. Q. He, G. Wang, L. Luo, Q. Shi, J. Xie, and X. Meng, “Mapping the managerial areas of building information modeling (bim) using scientometric analysis,” Int. J. Proj. Manag., vol. 35, no. 4, pp. 670–685, 2017.
24. D. Negrut, R. Serban, A. Elmquist, D. Hatch, E. Nutt, and P. Sheets, “Autonomous vehicles in the cyberspace: Accelerating testing via computer simulation,” SAE Technical Paper, Tech. Rep., 2018.
25. K. Shojaei, “Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque,” Ocean Eng., vol. 105, pp. 196–205, 2015.
26. A. A. Pereira, J. Binney, G. A. Hollinger, and G. S. Sukhatme, “Risk-aware path planning for autonomous underwater vehicles using predictive ocean models,” J. Field Robot., vol. 30, no. 5, pp. 741–762, 2013.
27. F. Trapsilawati, T. Wijayanto, and E. Jourdy, “Human-computer trust in navigation systems: google maps vs waze,” Comm. Sci. Tech., vol. 4, no. 1, pp. 38–43, 2019.
28. K. Kim, J. S. Kim, S. Jeong, J.-H. Park, and H. K. Kim, “Cybersecurity for autonomous vehicles: Review of attacks and defense,” Comput. Secur., vol. 103, p. 102150, 2021.
29. A. Khadka, P. Karypidis, A. Lytos, and G. Efstathopoulos, “A benchmarking framework for cyber-attacks on autonomous vehicles,” Transp. Res. Procedia, vol. 52, pp. 323–330, 2021.
30. S. Parkinson, P. Ward, K. Wilson, and J. Miller, “Cyber threats facing autonomous and connected vehicles: Future challenges,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 11, pp. 2898–2915, 2017.
31. L. Hansson, “Regulatory governance in emerging technologies: The case of autonomous vehicles in sweden and norway,” Res. Transp. Econ., vol. 83, p. 100967, 2020.
32. A. Miglani and N. Kumar, “Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges,” Veh. Commun., vol. 20, p. 100184, 2019.
33. J. Hua and A. Faghri, “Applications of artificial neural networks to intelligent vehicle-highway systems,” Transp. Res. Rec., vol. 1453, p. 83, 1994.
34. A. Stathopoulos, L. Dimitriou, and T. Tsekeris, “Fuzzy modeling approach for combined forecasting of urban traffic flow,” Comput.-Aided Civ. Infrastruct. Eng., vol. 23, no. 7, pp. 521–535, 2008.
35. B. L. Smith and M. J. Demetsky, “Traffic flow forecasting: comparison of modeling approaches,” J. Transp. Eng., vol. 123, no. 4, pp. 261–266, 1997.
36. S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” J. Field Robot., vol. 37, no. 3, pp. 362–386, 2020.
37. S. Shalev-Shwartz, S. Shammah, and A. Shashua, “Safe, multi-agent, reinforcement learning for autonomous driving,” arXiv:1610.03295, 2016.
38. S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, D. Rus, and M. H. Ang, “Perception, planning, control, and coordination for autonomous vehicles,” Machines, vol. 5, no. 1, p. 6, 2017.
39. E. Rehder, J. Quehl, and C. Stiller, “Driving like a human: Imitation learning for path planning using convolutional neural networks,” in Proc. Int. Conf. Robot. Autom. Workshops, 2017, pp. 1–5.
40. S. M. Grigorescu, B. Trasnea, L. Marina, A. Vasilcoi, and T. Cocias, “Neurotrajectory: A neuroevolutionary approach to local state trajectory learning for autonomous vehicles,” IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 3441–3448, 2019.
41. L. Sun, C. Peng, W. Zhan, and M. Tomizuka, “A fast integrated planning and control framework for autonomous driving via imitation learning,” in Proc. Dyn. Syst. Control Conf., vol. 51913. American Society of Mechanical Engineers, 2018, p. V003T37A012.
42. L. Yu, X. Shao, Y. Wei, and K. Zhou, “Intelligent land-vehicle model transfer trajectory planning method based on deep reinforcement learning,” Sensors, vol. 18, no. 9, p. 2905, 2018.
43. C. Paxton, V. Raman, G. D. Hager, and M. Kobilarov, “Combining neural networks and tree search for task and motion planning in challenging environments,” in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS). IEEE, 2017, pp. 6059–6066.
44. M. Naumann, H. Konigshof, M. Lauer, and C. Stiller, “Safe but not overcautious motion planning under occlusions and limited sensor range,” in Proc. IEEE Intell. Veh. Symp. (IV). IEEE, 2019, pp. 140–145.
45. M. Strickland, G. Fainekos, and H. B. Amor, “Deep predictive models for collision risk assessment in autonomous driving,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA). IEEE, 2018, pp. 4685–4692.
46. R. Lee, O. J. Mengshoel, A. Saksena, R. W. Gardner, D. Genin, J. Silbermann, M. Owen, and M. J. Kochenderfer, “Adaptive stress testing: Finding likely failure events with reinforcement learning,” J. Artif. Intell. Res., vol. 69, pp. 1165–1201, 2020.
47. D. Lu, H. Du, Z. Wu, and S. Yang, “Risk assessment in autonomous driving: a comprehensive survey of risk sources, methodologies, and system architectures,” Auton. Intell. Syst., vol. 5, no. 1, p. 24, 2025.