CoChat
Sign Up
Cited by 23Autonomous Vehicle Technology and SafetySafety Systems Engineering in AutonomySoftware Testing and Debugging Techniques

The DOI matches its Crossref record; the references it cites are not checked yet.

Testing and Verification of Connected and Autonomous Vehicles: A Review

Open access
Alemayehu, Hayleyesus, Sargolzaei, Arman·2025·Electronics
DOI 10.3390/electronics14030600
Open this paper in CoChat — free accountOpen PDF

Abstract

Connected and Autonomous Vehicles (CAVs) have the potential to revolutionize transportation by addressing critical challenges such as safety, energy efficiency, traffic congestion, and environmental impact. Realizing these benefits, however, requires the development of a rigorous testing and verification framework to enable the safe, efficient, and reliable deployment of CAVs across diverse operational scenarios. Despite the growing body of research, there remains a significant gap in review papers that comprehensively summarize recent studies related to the testing and verification of CAVs while identifying current challenges and highlighting future research directions. This paper seeks to address this gap by presenting a comprehensive review of the existing testing and verification frameworks for CAVs and identifying their associated challenges. Key topics covered include scenario generation, verification cost functions, assertion values, and security considerations. Furthermore, the paper highlights limitations within current frameworks, emphasizing the gaps that hinder systematic and comprehensive evaluations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/electronics14030600

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

The citation lists are queued

They are fetched later and appear here then.

References 159 · Cited by 23

Research works printed but not linked: 164

  1. Singh, 2016, Fatal road traffic accidents: Causes and factors responsible, J. Indian Acad. Forensic Med., 38, 52, 10.5958/0974-0848.2016.00014.2 · DOI
  2. Jovanov, 2019, Study of the impact of technical malfunctioning of vital vehicular parts on traffic safety, Teh. Vjesn., 26, 7
  3. Fountas, 2020, The joint effect of weather and lighting conditions on injury severities of single-vehicle accidents, Anal. Methods Accid. Res., 27, 100124
  4. Allaix, 2023, Existing standardization on monitoring, safety assessment and maintenance of bridges and tunnels, CE/Papers, 6, 498, 10.1002/cepa.2026 · DOI
  5. Agarwal, 2013, Development of a hierarchical structure to identify critical maintenance components affecting road safety, Procedia-Soc. Behav. Sci., 104, 292, 10.1016/j.sbspro.2013.11.122 · DOI
  6. Monzon, 2022, Combined influence of traffic conditions, driving behavior, and type of road on fuel consumption. Real driving data from Madrid Area, Int. J. Sustain. Transp., 16, 301, 10.1080/15568318.2020.1871128 · DOI
  7. Xu, H., Berres, A., Wang, C.R., LaClair, T.J., and Sanyal, J. (July, January 28). Visualizing vehicle acceleration and braking energy at intersections along a major traffic corridor. Proceedings of the Twelfth ACM International Conference on Future Energy Systems, Virtual Event.
  8. Afrin, T., and Yodo, N. (2020). A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 12. · DOI
  9. Sharma, 2019, Idling fuel consumption and emissions of air pollutants at selected signalized intersections in Delhi, J. Clean. Prod., 212, 8, 10.1016/j.jclepro.2018.11.275 · DOI
  10. Ibrahim, 2018, Socioeconomic impact assessment of highly dense-urban construction projects, Autom. Constr., 92, 230, 10.1016/j.autcon.2018.04.001 · DOI
  11. Montanaro, 2019, Towards connected autonomous driving: Review of use-cases, Veh. Syst. Dyn., 57, 779, 10.1080/00423114.2018.1492142 · DOI
  12. Olia, 2016, Assessing the potential impacts of connected vehicles: Mobility, environmental, and safety perspectives, J. Intell. Transp. Syst., 20, 229, 10.1080/15472450.2015.1062728 · DOI
  13. Elawady, A., Abuzwidah, M., and Zeiada, W. (2022, January 21–24). The benefits of using connected vehicles system on traffic delay and safety at urban signalized intersections. Proceedings of the 2022 Advances in Science and Engineering Technology International Conferences (ASET), Virtual Event. · DOI
  14. Doecke, 2015, The real-world safety potential of connected vehicle technology, Traffic Inj. Prev., 16, S31, 10.1080/15389588.2015.1014551 · DOI
  15. Papadoulis, 2019, Evaluating the safety impact of connected and autonomous vehicles on motorways, Accid. Anal. Prev., 124, 12, 10.1016/j.aap.2018.12.019 · DOI
  16. Neufville, R., Abdalla, H., and Abbas, A. (2022). Potential of connected fully autonomous vehicles in reducing congestion and associated carbon emissions. Sustainability, 14. · DOI
  17. Motallebi, 2021, Route intersection reduction with connected autonomous vehicles, GeoInformatica, 25, 99, 10.1007/s10707-020-00420-z · DOI
  18. Talebpour, 2016, Influence of connected and autonomous vehicles on traffic flow stability and throughput, Transp. Res. Part C Emerg. Technol., 71, 143, 10.1016/j.trc.2016.07.007 · DOI · Paper page
  19. Faghihian, H., and Sargolzaei, A. (2023). Energy efficiency of connected autonomous vehicles: A review. Electronics, 12. · DOI
  20. Wang, 2019, Cooperative eco-driving at signalized intersections in a partially connected and automated vehicle environment, IEEE Trans. Intell. Transp. Syst., 21, 2029, 10.1109/TITS.2019.2911607 · DOI
  21. Liang, 2022, A MAS-based hierarchical architecture for the cooperation control of connected and automated vehicles, IEEE Trans. Veh. Technol., 72, 1559, 10.1109/TVT.2022.3211733 · DOI
  22. Guanetti, 2018, Control of connected and automated vehicles: State of the art and future challenges, Annu. Rev. Control., 45, 18, 10.1016/j.arcontrol.2018.04.011 · DOI
  23. Eskandarian, 2019, Research advances and challenges of autonomous and connected ground vehicles, IEEE Trans. Intell. Transp. Syst., 22, 683, 10.1109/TITS.2019.2958352 · DOI
  24. Yang, 2021, Machine-learning-enabled cooperative perception for connected autonomous vehicles: Challenges and opportunities, IEEE Netw., 35, 96, 10.1109/MNET.011.2000560 · DOI
  25. Butt, 2022, On the integration of enabling wireless technologies and sensor fusion for next-generation connected and autonomous vehicles, IEEE Access, 10, 14643, 10.1109/ACCESS.2022.3145972 · DOI
  26. Qayyum, 2020, Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward, IEEE Commun. Surv. Tutorials, 22, 998, 10.1109/COMST.2020.2975048 · DOI · Paper page
  27. Cano, L.B., Raza, S., Sekibo, B., Siafaras, A., Wolf, Q., Yin, Z., Xu, W., and Tuncer, D. (2022, January 22–26). A comparative perspective of data regulation frameworks and their implications for connected vehicles. Proceedings of the ACM SIGCOMM Joint Workshops on Technologies, Applications, and Uses of a Responsible Internet and Building Greener Internet, Beurs van Berlage Conference Venue, Amsterdam, The Netherlands. · DOI
  28. Lee, 2020, Regulations for on-road testing of connected and automated vehicles: Assessing the potential for global safety harmonization, Transp. Res. Part A Policy Pract., 136, 85, 10.1016/j.tra.2020.03.026 · DOI
  29. Zhang, 2019, The roles of initial trust and perceived risk in public’s acceptance of automated vehicles, Transp. Res. Part C Emerg. Technol., 98, 207, 10.1016/j.trc.2018.11.018 · DOI
  30. Lee, 2021, Media influence, trust, and the public adoption of automated vehicles, IEEE Intell. Transp. Syst. Mag., 14, 174, 10.1109/MITS.2021.3082404 · DOI
  31. Cucor, B., Petrov, T., Kamencay, P., Pourhashem, G., and Dado, M. (2022). Physical and digital infrastructure readiness index for Connected and Automated Vehicles. Sensors, 22. · DOI
  32. Tengilimoglu, 2023, Infrastructure-related challenges in implementing connected and automated vehicles on urban roads: Insights from experts and stakeholders, IET Intell. Transp. Syst., 17, 2352, 10.1049/itr2.12413 · DOI
  33. Koopman, 2017, Autonomous vehicle safety: An interdisciplinary challenge, IEEE Intell. Transp. Syst. Mag., 9, 90, 10.1109/MITS.2016.2583491 · DOI · Paper page
  34. Pereira, J., Premebida, C., Asvadi, A., Cannata, F., Garrote, L., and Nunes, U. (2019, January 9–12). Test and evaluation of connected and autonomous vehicles in real-world scenarios. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France. · DOI
  35. Holland, 2024, A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks, IEEE Access, 12, 19848, 10.1109/ACCESS.2024.3357357 · DOI
  36. Ma, J., Che, X., Li, Y., and Lai, E.M.K. (2021). Traffic scenarios for automated vehicle testing: A review of description languages and systems. Machines, 9. · DOI
  37. Cai, J., Deng, W., Guang, H., Wang, Y., Li, J., and Ding, J. (2022). A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing. Machines, 10. · DOI
  38. Mavromatis, I., Spyridopoulos, T., Carnelli, P., Chin, W.H., Khalil, A., Chakravarty, J., Kun, L.C., Piechocki, R.J., Robbins, C., and Cunnington, D. (2023). Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs. arXiv. · DOI
  39. Rajabli, 2020, Software verification and validation of safe autonomous cars: A systematic literature review, IEEE Access, 9, 4797, 10.1109/ACCESS.2020.3048047 · DOI
  40. Wang, J., Shao, Y., Ge, Y., and Yu, R. (2019). A survey of vehicle to everything (V2X) testing. Sensors, 19. · DOI
  41. Haspolat, C., Celik, E., Aygun, M., Cosar, I., and Karaduman, M. (December, January 30). A Review Study on Functional Safety Verification and Validation for Autonomous Vehicles. Proceedings of the 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey. · DOI
  42. Frischknecht-Gruber, C., Ricchiuto, B., Reif, M., Weng, J., and Senn, C. (2023, January 3–7). Ensuring Safety in Highly Automated Vehicles: A Review of Testing and Validation Methods for Robustness and Reliability. Proceedings of the 33rd European Safety and Reliability Conference (ESREL), Southampton, UK. · DOI
  43. Ma, 2022, Verification and validation methods for decision-making and planning of automated vehicles: A review, IEEE Trans. Intell. Veh., 7, 480, 10.1109/TIV.2022.3196396 · DOI
  44. Tahir, Z., and Alexander, R. (2020, January 26–28). Coverage based testing for V&V and safety assurance of self-driving autonomous vehicles: A systematic literature review. Proceedings of the 2020 IEEE International Conference On Artificial Intelligence Testing (AITest), Virtual Event. · DOI
  45. Huang, W., Wang, K., Lv, Y., and Zhu, F. (2016, January 1–4). Autonomous vehicles testing methods review. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil. · DOI
  46. Sun, 2021, Scenario-based test automation for highly automated vehicles: A review and paving the way for systematic safety assurance, IEEE Trans. Intell. Transp. Syst., 23, 14088, 10.1109/TITS.2021.3136353 · DOI
  47. Ciuffo, 2022, Virtual testing of automated driving systems. A survey on validation methods, IEEE Access, 10, 24349, 10.1109/ACCESS.2022.3153722 · DOI
  48. Pietruch, 2020, An overview and review of testing methods for the verification and validation of ADAS, active safety systems, and autonomous driving, Min. Inform. Autom. Electr. Eng., 58, 19
  49. Araujo, 2023, Testing, validation, and verification of robotic and autonomous systems: A systematic review, ACM Trans. Softw. Eng. Methodol., 32, 1, 10.1145/3542945 · DOI
  50. Zhang, T., Liu, H., Wang, W., and Wang, X. (2024). Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions. Electronics, 13. · DOI
  51. Wishart, 2020, Literature review of verification and validation activities of automated driving systems, SAE Int. J. Connect. Autom. Veh., 3, 267, 10.4271/12-03-04-0020 · DOI
  52. Li, 2023, Emergent visual sensors for autonomous vehicles, IEEE Trans. Intell. Transp. Syst., 24, 4716, 10.1109/TITS.2023.3248483 · DOI
  53. Ayala, 2021, Sensors in autonomous vehicles: A survey, J. Auton. Veh. Syst., 1, 031003
  54. Yusuf, 2024, Vehicle-to-everything (V2X) in the autonomous vehicles domain—A technical review of communication, sensor, and AI technologies for road user safety, Transp. Res. Interdiscip. Perspect., 23, 100980
  55. Xu, Q., Liu, W., Su, Z., and Zhang, W. (2022, January 21–23). Review on the evolution process and key technologies of V2X-based protocol. Proceedings of the 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China. · DOI
  56. Arena, F., and Pau, G. (2019). An overview of vehicular communications. Future Internet, 11. · DOI
  57. Adegoke, 2019, Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art, Veh. Commun., 20, 100185
  58. Fahmy, A., Altaf, H., Al Nabulsi, A., Al-Ali, A., and Aburukba, R. (2019, January 19–21). Role of RFID technology in smart city applications. Proceedings of the 2019 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA), Sharjah, United Arab Emirates. · DOI
  59. Ahn, J.H., and Won, J.H. (2020, January 20–23). Localization based on lidar and GNSS for connected vehicles. Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA. · DOI
  60. Arnold, 2020, Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors, IEEE Trans. Intell. Transp. Syst., 23, 1852, 10.1109/TITS.2020.3028424 · DOI
  61. Cui, G., Zhang, W., Xiao, Y., Yao, L., and Fang, Z. (2022). Cooperative perception technology of autonomous driving in the internet of vehicles environment: A review. Sensors, 22. · DOI
  62. Wang, 2021, Competitive and cooperative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach, Transp. Res. Part B Methodol., 149, 322, 10.1016/j.trb.2021.05.007 · DOI
  63. Li, 2024, A Hierarchical Intention Inference Model for Connected and Automated Vehicles, IEEE Trans. Intell. Veh., 14, 1471
  64. Ozkan, 2021, Eco-driving of connected and automated vehicle with preceding driver behavior prediction, J. Dyn. Syst. Meas. Control., 143, 011002, 10.1115/1.4048108 · DOI
  65. Viana, Í.B., Kanchwala, H., and Aouf, N. (2019, January 4–8). Cooperative trajectory planning for autonomous driving using nonlinear model predictive control. Proceedings of the 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria. · DOI
  66. Yu, 2019, Corridor level cooperative trajectory optimization with connected and automated vehicles, Transp. Res. Part C Emerg. Technol., 105, 405, 10.1016/j.trc.2019.06.002 · DOI
  67. Cao, Y., Li, B., Zhang, H., Chu, D., Huang, Y., and Guo, X. (2022, January 28–30). Cooperative Motion Planning with 3D Spatio-temporal Map for Connected and Automated Vehicles in Intersection. Proceedings of the 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), Nanjing, China. · DOI
  68. Li, 2018, Fault-tolerant cooperative motion planning of connected and automated vehicles at a signal-free and lane-free intersection, IFAC Pap. Online, 51, 60, 10.1016/j.ifacol.2018.09.529 · DOI
  69. Gong, X., Wang, B., and Liang, S. (2024). Collision-Free Cooperative Motion Planning and Decision-Making for Connected and Automated Vehicles at Unsignalized Intersections. IEEE Trans. Syst. Man Cybern. Syst. · DOI
  70. Liang, 2023, A polytopic model-based robust predictive control scheme for path tracking of autonomous vehicles, IEEE Trans. Intell. Veh., 9, 3928, 10.1109/TIV.2023.3340668 · DOI
  71. Mahbub, 2021, A platoon formation framework in a mixed traffic environment, IEEE Control. Syst. Lett., 6, 1370, 10.1109/LCSYS.2021.3092188 · DOI
  72. Firoozi, 2021, Formation and reconfiguration of tight multi-lane platoons, Control. Eng. Pract., 108, 104714, 10.1016/j.conengprac.2020.104714 · DOI
  73. Larson, 2014, A distributed framework for coordinated heavy-duty vehicle platooning, IEEE Trans. Intell. Transp. Syst., 16, 419, 10.1109/TITS.2014.2320133 · DOI
  74. Soni, A., and Hu, H. (2018). Formation control for a fleet of autonomous ground vehicles: A survey. Robotics, 7. · DOI
  75. Ge, 2021, Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks, IEEE/CAA J. Autom. Sin., 9, 31, 10.1109/JAS.2021.1004060 · DOI
  76. Zhang, R., Jacquemot, B., Bakirci, K., Bartholme, S., Kaempf, K., Freydt, B., Montandon, L., Zhang, S., and Tonguz, O.K. (2020, January 25–28). Leader selection in vehicular ad-hoc networks: A proactive approach. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium. · DOI
  77. Ying, 2021, A reputation-based leader election scheme for opportunistic autonomous vehicle platoon, IEEE Trans. Veh. Technol., 71, 3519, 10.1109/TVT.2021.3106297 · DOI
  78. Wang, Z., Wu, G., and Barth, M.J. (2018, January 4–7). A review on cooperative adaptive cruise control (CACC) systems: Architectures, controls, and applications. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA. · DOI
  79. Gong, 2018, Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles, Transp. Res. Part B Methodol., 116, 25, 10.1016/j.trb.2018.07.005 · DOI
  80. Zhang, 2018, Cooperative adaptive cruise control in mixed traffic with selective use of vehicle-to-vehicle communication, IET Intell. Transp. Syst., 12, 1243, 10.1049/iet-its.2018.5235 · DOI
  81. Ding, 2021, Automated cooperative control of multilane freeway merging areas in connected and autonomous vehicle environments, Transp. B Transp. Dyn., 9, 437
  82. Liu, 2023, Safety-critical and flexible cooperative on-ramp merging control of connected and automated vehicles in mixed traffic, IEEE Trans. Intell. Transp. Syst., 24, 2920, 10.1109/TITS.2022.3224592 · DOI
  83. Chen, 2020, A hierarchical model-based optimization control approach for cooperative merging by connected automated vehicles, IEEE Trans. Intell. Transp. Syst., 22, 7712, 10.1109/TITS.2020.3007647 · DOI
  84. Jing, 2019, Cooperative game approach to optimal merging sequence and on-ramp merging control of connected and automated vehicles, IEEE Trans. Intell. Transp. Syst., 20, 4234, 10.1109/TITS.2019.2925871 · DOI
  85. Malikopoulos, 2016, Automated and cooperative vehicle merging at highway on-ramps, IEEE Trans. Intell. Transp. Syst., 18, 780
  86. Ding, 2019, A rule-based cooperative merging strategy for connected and automated vehicles, IEEE Trans. Intell. Transp. Syst., 21, 3436, 10.1109/TITS.2019.2928969 · DOI
  87. Guo, 2020, Merging and diverging impact on mixed traffic of regular and autonomous vehicles, IEEE Trans. Intell. Transp. Syst., 22, 1639, 10.1109/TITS.2020.2974291 · DOI
  88. Zheng, 2019, Cooperative lane changing strategies to improve traffic operation and safety nearby freeway off-ramps in a connected and automated vehicles environment, IEEE Trans. Intell. Transp. Syst., 21, 4605, 10.1109/TITS.2019.2942050 · DOI
  89. Li, 2020, A cooperative lane change model for connected and automated vehicles, IEEE Access, 8, 54940, 10.1109/ACCESS.2020.2981169 · DOI
  90. Sun, 2021, A cooperative lane change model for connected and autonomous vehicles on two lanes highway by considering the traffic efficiency on both lanes, Transp. Res. Interdiscip. Perspect., 9, 100310
  91. Duan, 2023, Cooperative lane-change motion planning for connected and automated vehicle platoons in multi-lane scenarios, IEEE Trans. Intell. Transp. Syst., 24, 7073, 10.1109/TITS.2023.3253479 · DOI
  92. Deng, 2019, Cooperative collision avoidance for overtaking maneuvers in cellular V2X-based autonomous driving, IEEE Trans. Veh. Technol., 68, 4434, 10.1109/TVT.2019.2906509 · DOI
  93. Prathiba, 2021, Intelligent cooperative collision avoidance at overtaking and lane changing maneuver in 6G-V2X communications, IEEE Trans. Veh. Technol., 71, 112, 10.1109/TVT.2021.3127219 · DOI
  94. Mertens, J.C., Hauenstein, J., Diermeyer, F., Jahn, L., and Kraus, S. (2020, January 10–12). Cooperative truck overtaking on freeways. Proceedings of the 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco. · DOI
  95. Dong, 2023, Overtaking-enabled eco-approach control at signalized intersections for connected and automated vehicles, IEEE Trans. Intell. Transp. Syst., 25, 4527, 10.1109/TITS.2023.3328022 · DOI
  96. Lv, 2022, Cooperative decision-making of connected and autonomous vehicles in an emergency, IEEE TRansactions Veh. Technol., 72, 1464, 10.1109/TVT.2022.3211884 · DOI
  97. Hu, 2022, Distributed coordinated brake control for longitudinal collision avoidance of multiple connected automated vehicles, IEEE Trans. Intell. Veh., 8, 745, 10.1109/TIV.2022.3197951 · DOI
  98. Wang, 2015, Improved cooperative collision avoidance (CCA) model considering driver comfort, Int. J. Automot. Technol., 16, 989, 10.1007/s12239-015-0101-7 · DOI
  99. Hasan, S., Girs, S., and Uhlemann, E. (2023, January 16–18). Cooperative automated emergency braking for cavs under time-varying communication delays. Proceedings of the 2023 IEEE International Automated Vehicle Validation Conference (IAVVC), Austin, TX, USA. · DOI
  100. Aliedani, 2019, Cooperative car parking using vehicle-to-vehicle communication: An agent-based analysis, Comput. Environ. Urban Syst., 77, 101256, 10.1016/j.compenvurbsys.2018.06.002 · DOI
  101. Alarbi, 2023, SCOPE: Smart Cooperative Parking Environment, IEEE Access, 11, 120346, 10.1109/ACCESS.2023.3328233 · DOI
  102. Said, 2021, An intelligent parking sharing system for green and smart cities based IoT, Comput. Commun., 172, 10, 10.1016/j.comcom.2021.02.017 · DOI
  103. Atif, 2016, Internet of things approach to cloud-based smart car parking, Procedia Comput. Sci., 98, 193, 10.1016/j.procs.2016.09.031 · DOI
  104. Lin, 2017, A survey of smart parking solutions, IEEE Trans. Intell. Transp. Syst., 18, 3229, 10.1109/TITS.2017.2685143 · DOI
  105. Correa, A., Boquet, G., Morell, A., and Lopez Vicario, J. (2017). Autonomous car parking system through a cooperative vehicular positioning network. Sensors, 17. · DOI
  106. Aliedani, A., Loke, S.W., Desai, A., and Desai, P. (2016, January 12–15). Investigating vehicle-to-vehicle communication for cooperative car parking: The copark approach. Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy. · DOI
  107. Guo, 2019, Urban traffic signal control with connected and automated vehicles: A survey, Transp. Res. Part C Emerg. Technol., 101, 313, 10.1016/j.trc.2019.01.026 · DOI
  108. Jurczenia, 2022, A survey of vehicular network systems for road traffic management, IEEE Access, 10, 42365, 10.1109/ACCESS.2022.3168354 · DOI
  109. Khayatian, 2020, A survey on intersection management of connected autonomous vehicles, ACM Trans.-Cyber-Phys. Syst., 4, 1, 10.1145/3407903 · DOI
  110. Bahaaldin, 2017, A Case Study on the Impacts of Connected Vehicle Technology on No-Notice Evacuation Clearance Time, J. Adv. Transp., 2017, 6357415, 10.1155/2017/6357415 · DOI
  111. Abraham, A., Zhang, Y., and Prasad, S. (2024). Evacuation Management Framework towards Smart City-wide Intelligent Emergency Interactive Response System. arXiv.
  112. Hannoun, 2018, Facilitating emergency response vehicles’ movement through a road segment in a connected vehicle environment, IEEE Trans. Intell. Transp. Syst., 20, 3546, 10.1109/TITS.2018.2877758 · DOI
  113. Chang, 2018, Evaluation of a reservation-based intersection control algorithm for hurricane evacuation with autonomous vehicles, Int. J. Disaster Risk Reduct., 31, 1152, 10.1016/j.ijdrr.2017.09.001 · DOI
  114. Zhao, 2022, Modeling post-shock emergency transfers with the participation of connected-and-autonomous vehicles, Int. J. Disaster Risk Reduct., 83, 103436, 10.1016/j.ijdrr.2022.103436 · DOI
  115. Memar, F.H., Qiao, C., and Sadek, A.W. (2022, January 20–22). Edge-AI Enabled Automated Flaggers for Roadway Work Zone Management. Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, CO, USA. · DOI
  116. Mintsis, E., Lücken, L., Karagounis, V., Porfyri, K., Rondinone, M., Correa, A., Schindler, J., and Mitsakis, E. (2020, January 20–23). Joint deployment of infrastructure-assisted traffic management and cooperative driving around work zones. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Virtual Event. · DOI
  117. Darwesh, A., Wu, D., Le, M., and Saripalli, S. (2021, January 19–22). Building a smart work zone using roadside lidar. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA. · DOI
  118. Knickerbocker, 2021, Deploying and integrating smart devices to improve work zone data for work zone data exchange, Transp. Res. Rec., 2675, 317, 10.1177/03611981211011481 · DOI
  119. Alnaser, 2019, Autonomous vehicles scenario testing framework and model of computation, SAE Int. J. Connect. Autom. Veh., 2, 205, 10.4271/12-02-04-0015 · DOI
  120. Sargolzaei, 2021, Autonomous vehicles scenario testing framework and model of computation: On generation and coverage, IEEE Access, 9, 60617, 10.1109/ACCESS.2021.3074062 · DOI
  121. Alnaser, 2024, Employing a Model of Computation for Testing and Verifying the Security of Connected and Autonomous Vehicles, SAE Int. J. Connect. Autom. Veh., 7, 16
  122. Zaidi, 2022, A survey of modern deep learning based object detection models, Digit. Signal Process., 126, 103514, 10.1016/j.dsp.2022.103514 · DOI
  123. Obi, 2023, A comparative study of several classification metrics and their performances on data, World J. Adv. Eng. Technol. Sci., 8, 308, 10.30574/wjaets.2023.8.1.0054 · DOI
  124. Wang, B. (2022). A parallel implementation of computing mean average precision. arXiv.
  125. Swaroop, 1996, String stability of interconnected systems, IEEE Trans. Autom. Control., 41, 349, 10.1109/9.486636 · DOI
  126. Feng, 2019, String stability for vehicular platoon control: Definitions and analysis methods, Annu. Rev. Control., 47, 81, 10.1016/j.arcontrol.2019.03.001 · DOI
  127. Shaw, E., and Hedrick, J.K. (2007, January 11–13). String stability analysis for heterogeneous vehicle strings. Proceedings of the 2007 American Control Conference, New York, NY, USA. · DOI
  128. Malikopoulos, 2018, Impact of partial penetrations of connected and automated vehicles on fuel consumption and traffic flow, IEEE Trans. Intell. Veh., 3, 453, 10.1109/TIV.2018.2873899 · DOI
  129. Houshmand, A., Wollenstein-Betech, S., and Cassandras, C.G. (2019, January 27–30). The penetration rate effect of connected and automated vehicles in mixed traffic routing. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand. · DOI
  130. Rios-Torres, J., and Malikopoulos, A.A. (November, January 31). Energy impact of different penetrations of connected and automated vehicles: A preliminary assessment. Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, Burlingame, CA, USA.
  131. Medrano-Berumen, C., and Akbaş, M.İ. (2020, January 23–26). Scenario generation for validating artificial intelligence based autonomous vehicles. Proceedings of the Intelligent Information and Database Systems: 12th Asian Conference, ACIIDS 2020, Phuket, Thailand. Part II 12. · DOI
  132. Masuda, 2018, Rule-based searching for collision test cases of autonomous vehicles simulation, IET Intell. Transp. Syst., 12, 1088, 10.1049/iet-its.2018.5335 · DOI
  133. Deng, Y., Yao, J., Tu, Z., Zheng, X., Zhang, M., and Zhang, T. (2023). Target: Automated scenario generation from traffic rules for testing autonomous vehicles. arXiv.
  134. Zhai, 2023, Research on the Data Collection and Scenario Generation Method for Automated Vehicles, Proc. J. Physics Conf. Ser. Iop Publ., 2665, 012002, 10.1088/1742-6596/2665/1/012002 · DOI
  135. Beglerovic, H., Stolz, M., and Horn, M. (2017, January 16–19). Testing of autonomous vehicles using surrogate models and stochastic optimization. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan. · DOI
  136. Klischat, M., and Althoff, M. (2019, January 9–12). Generating critical test scenarios for automated vehicles with evolutionary algorithms. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France. · DOI
  137. Tuncali, C.E., and Fainekos, G. (2019, January 7–30). Rapidly-exploring random trees for testing automated vehicles. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand. · DOI
  138. Ben Abdessalem, R., Nejati, S., Briand, L.C., and Stifter, T. (2016, January 3–7). Testing advanced driver assistance systems using multi-objective search and neural networks. Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, Singapore. · DOI
  139. Gambi, A., Mueller, M., and Fraser, G. (2019, January 15–19). Automatically testing self-driving cars with search-based procedural content generation. Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, Beijing, China. · DOI
  140. Althoff, M., and Lutz, S. (2018, January 26–30). Automatic generation of safety-critical test scenarios for collision avoidance of road vehicles. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, Suzhou, China. · DOI
  141. Holland, 2020, Verification of autonomous vehicles: Scenario generation based on real world accidents, Proceedings of the 2020 SoutheastCon, Volume 2, 1
  142. Li, 2023, Advanced scenario generation for calibration and verification of autonomous vehicles, IEEE Trans. Intell. Veh., 8, 3211, 10.1109/TIV.2023.3269428 · DOI
  143. Laurent, 2023, Parameter coverage for testing of autonomous driving systems under uncertainty, ACM Trans. Softw. Eng. Methodol., 32, 1, 10.1145/3550270 · DOI
  144. Huang, L., Liang, H., Yu, B., Li, B., and Zhu, H. (2019, January 13–15). Ontology-based driving scene modeling, situation assessment and decision making for autonomous vehicles. Proceedings of the 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Nagoya, Japan. · DOI
  145. Li, 2020, Ontology-based test generation for automated and autonomous driving functions, Inf. Softw. Technol., 117, 106200, 10.1016/j.infsof.2019.106200 · DOI
  146. Zipfl, M., Koch, N., and Zöllner, J.M. (2023, January 4–7). A comprehensive review on ontologies for scenario-based testing in the context of autonomous driving. Proceedings of the 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, Alaska. · DOI
  147. Hsiang, H., Chen, K.C., and Chen, Y.Y. (2022, January 22–25). Development of Simulation-Based Testing Scenario Generator for Robustness Verification of Autonomous Vehicles. Proceedings of the 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), Hammamet, Tunisia. · DOI
  148. Ren, H., Gao, H., Chen, H., and Liu, G. (2022, January 25–26). A survey of autonomous driving scenarios and scenario databases. Proceedings of the 2022 9th International Conference on Dependable Systems and Their Applications (DSA), Urumqi, China. · DOI
  149. Lu, C., Yue, T., and Ali, S. (2023, January 15–16). DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing. Proceedings of the 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), Melbourne, Australia. · DOI
  150. Raghupatruni, I., Goeppel, T., Atak, M., Bou, J., and Huber, T. (2019, January 4–8). Empirical testing of automotive cyber-physical systems with credible software-in-the-loop environments. Proceedings of the 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria. · DOI
  151. Mihalič, F., Truntič, M., and Hren, A. (2022). Hardware-in-the-loop simulations: A historical overview of engineering challenges. Electronics, 11. · DOI
  152. Kim, Y., Tay, S., Guanetti, J., Borrelli, F., and Miller, R. (2019). Hardware-in-the-loop for connected automated vehicles testing in real traffic. arXiv. · DOI
  153. Gelbal, Ş.Y., Tamilarasan, S., Cantaş, M.R., Güvenç, L., and Aksun-Güvenç, B. (2017, January 5–8). A connected and autonomous vehicle hardware-in-the-loop simulator for developing automated driving algorithms. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada. · DOI
  154. Fayazi, 2019, A Vehicle-in-the-Loop (VIL) verification of an all-autonomous intersection control scheme, Transp. Res. Part C Emerg. Technol., 107, 193, 10.1016/j.trc.2019.07.027 · DOI
  155. Gupta, P., Wang, R., Ard, T., Han, J., Karbowski, D., Vahidi, A., and Jia, Y. (2023, January 19–21). An X-in-the-Loop (XIL) Testing Framework for Validation of Connected and Autonomous Vehicles. Proceedings of the 2023 IEEE International Automated Vehicle Validation Conference (IAVVC), Austin, TX, USA. · DOI
  156. Previati, 2023, Roundabout Traffic: Simulation With Automated Vehicles, AI, 5G, Edge Computing and Human in the Loop, Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 87288, V001T01A016
  157. Rajendran, A.V., Hegde, B., Ahmed, Q., and Rizzoni, G. (2017, January 27–30). Design and development of traffic-in-loop powertrain simulation. Proceedings of the 2017 IEEE Conference on Control Technology and Applications (CCTA), Kohala Coast, HI, USA. · DOI
  158. Rong, G., Shin, B.H., Tabatabaee, H., Lu, Q., Lemke, S., Možeiko, M., Boise, E., Uhm, G., Gerow, M., and Mehta, S. (2020, January 20–23). Lgsvl simulator: A high fidelity simulator for autonomous driving. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Virtual Event. · DOI
  159. IEEE Spectrum (2025, January 18). Apex.OS: An Open-Source Operating System for Autonomous Cars. Available online: https://spectrum.ieee.org/apexos-operating-system-open-source-autonomous-cars.
  160. Fremont, D.J., Kim, E., Pant, Y.V., Seshia, S.A., Acharya, A., Bruso, X., Wells, P., Lemke, S., Lu, Q., and Mehta, S. (2020, January 20–23). Formal scenario-based testing of autonomous vehicles: From simulation to the real world. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Virtual Event. · DOI
  161. ACM Willow Run (2025, January 18). American Center for Mobility: Willow Run. Available online: https://acmwillowrun.org/.
  162. Gupta, 2023, An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles, IEEE Access, 11, 2169, 10.1109/ACCESS.2023.3307473 · DOI
  163. Niroumand, 2024, Security of Connected and Autonomous Vehicles: A Review of Attacks and Mitigation Strategies, SoutheastCon, 2024, 1197
  164. Sharma, 2022, Cybersecurity and forensics in connected autonomous vehicles: A review of the state-of-the-art, IEEE Access, 10, 108979, 10.1109/ACCESS.2022.3213843 · DOI

Metadata

Authors
Alemayehu, H., & Sargolzaei, A.
Affiliations
University of South Florida (US)
Corresponding author
Alemayehu, Hayleyesus
ORCID
Venue
Electronics
Publisher
MDPI AG
Published
2025-02-03
Volume
14
Issue
3
Pages
600
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics14030600
OpenAlex
W4407098284
Cited by
23
References
159
Citations by year
2026: 15 · 2025: 8
FWCI
5.60
Type
journal article
Language
en
Open access
Licence
Locations
Electronics (publishedVersion, cc-by)
Funders
National Science Foundation (ECCS-EPCN-2241718)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords
connected and autonomous vehicles, verification framework, scenario generation, assertion, cybersecurity, autonomous driving safety, assessment challenges

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Testing and Verification of Connected and Autonomous Vehicles: A Review. https://app.cochat.ai/papers/10.3390/electronics14030600
The paper
Alemayehu, H., & Sargolzaei, A. (2025). Testing and Verification of Connected and Autonomous Vehicles: A Review. Electronics. https://doi.org/10.3390/electronics14030600