The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
Open accessAbstract
This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.
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.
Integrity signals
Registry
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The Global Positioning System (GPS) is the most common GNSS system freely available to be used in any part of the world [111].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [111]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is mentioned that a pure vision-based system has advantages like affordability and simplicity of implementation.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [48]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Such simulation not only includes software simulation like CarSim and CARLA. but also includes hybrid realization with hardware-in-the-loop or vehicle-in-the-loop design [67].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [67]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Such simulation not only includes software simulation like CarSim and CARLA. but also includes hybrid realization with hardware-in-the-loop or vehicle-in-the-loop design [67].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [67]
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
Cites
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
Kalra, N., & Paddock, S. M. (2016) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2016.09.010 · Cited by 1,221
A Review of Data Fusion Techniques
Castanedo, F. (2013) · The Scientific World Journal · doi:10.1155/2013/704504 · Cited by 1,027
Thermal cameras and applications: A survey
Gade, R., & Moeslund, T. B. (2014) · Machine Vision and Applications · doi:10.1007/s00138-013-0570-5 · Cited by 780
Challenges in autonomous vehicle testing and validation
Koopman, P., & Wagner, M. (2016) · SAE International Journal of Transportation Safety · doi:10.4271/2016-01-0128 · Cited by 675
USARSim: a robot simulator for research and education
Carpin, S., Lewis, M., Wang, J., Balakirsky, S. B., & Scrapper, C. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363180 · Cited by 423
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison
González, A., Fang, Z., Socarras, Y., Serrat, J., Vázquez, D., Xu, J., & López, A. M. (2016) · Sensors · doi:10.3390/s16060820 · Cited by 330
Bimbraw, K. (2015) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0005540501910198 · Cited by 324
Development of Autonomous Car—Part I: Distributed System Architecture and Development Process
Jo, K., Kim, J., Kim, D., Jang, C., & Sunwoo, M. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2321342 · Cited by 277
Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations
Gietelink, O. J., Ploeg, J., Schutter, B. D., & Verhaegen, M. (2006) · Vehicle System Dynamics · doi:10.1080/00423110600563338 · Cited by 272
Flagged first, then by citations
Research works printed but not linked: 2
- Council, N.R. (2001). Statistics, Testing, and Defense Acquisition: New Approaches and Methodological Improvements, The National Academies Press.
- Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (arXiv, 2017). CARLA: An Open Urban Driving Simulator, arXiv.
Cited by
Sorted by citations
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. L. (2021) · Sensors · doi:10.3390/s21062140 · Cited by 1,187
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
Aradi, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3024655 · Cited by 704
Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021) · Array · doi:10.1016/j.array.2021.100057 · Cited by 609
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
Multi-Sensor Fusion in Automated Driving: A Survey
Wang, Z., Wu, Y., & Niu, Q. (2020) · IEEE Access · doi:10.1109/access.2019.2962554 · Cited by 526
An Overview of Lidar Imaging Systems for Autonomous Vehicles
Royo, S., & Ballesta-Garcia, M. (2019) · Applied Sciences · doi:10.3390/app9194093 · Cited by 505
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 467
A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
Ahangar, M. N., Ahmed, Q. Z., Khan, F. A., & Hafeez, M. (2021) · Sensors · doi:10.3390/s21030706 · Cited by 410
An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
Vargas, J., Alsweiss, S., Toker, O., Razdan, R., & Santos, J. A. (2021) · Sensors · doi:10.3390/s21165397 · Cited by 416
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
Yao, S., Guan, R., Huang, X., Li, Z., Sha, X., Yue, Y., Lim, E. G., Seo, H., Man, K. L., Zhu, X., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3307157 · Cited by 312
Johnston, A., & Carneiro, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00481 · Cited by 281
Qayyum, A., Usama, M., Qadir, J., & Al-Fuqaha, A. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975048 · Cited by 257
Model predictive path tracking control for automated road vehicles: A review
Stano, P., Montanaro, U., Tavernini, D., Tufo, M., Fiengo, G., Novella, L., & Sorniotti, A. (2022) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2022.11.001 · Cited by 206
Hu, J.-W., Zheng, B., Wang, C., Zhao, C.-H., Hou, X., Pan, Q., & Zhao, X. (2020) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900518 · Cited by 183
Autonomous Shuttle Bus for Public Transportation: A Review
Iclodean, C. D., Cordoș, N., & Varga, B. O. (2020) · Energies · doi:10.3390/en13112917 · Cited by 169
Khan, S. K., Shiwakoti, N., Stasinopoulos, P., & Chen, Y. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105837 · Cited by 176
Mohammed, A. S., Amamou, A., Ayevide, F. K., Kélouwani, S., Agbossou, K., & Zioui, N. (2020) · Sensors · doi:10.3390/s20226532 · Cited by 170
A Survey on Automated Driving System Testing: Landscapes and Trends
Tang, S., Zhang, Z., Zhang, Y., Zhou, J., Guo, Y. R., Liu, S., Guo, S., Li, Y., Ma, L., Xue, Y., et al. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3579642 · Cited by 187
Chen, L. F., Li, Y., Huang, C., Xing, Y., Tian, D., Li, L., Hu, Z., Teng, S., Lv, C., Wang, J., et al. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3276218 · Cited by 146
Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145
Implications of automated vehicles for physical road environment: A comprehensive review
Tengilimoglu, O., Carsten, O., & Wadud, Z. (2023) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2022.102989 · Cited by 149
High-Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives
Elghazaly, G., Frank, R., Harvey, S., & Safko, S. (2023) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2023.3295502 · Cited by 173
A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways
Ristić–Durrant, D., Franke, M., & Michels, K. (2021) · Sensors · doi:10.3390/s21103452 · Cited by 143
Cui, G., Zhang, W., Xiao, Y., Yao, L. Y., & Fang, Z. (2022) · Sensors · doi:10.3390/s22155535 · Cited by 141
Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning
Zubler, A. V., & Yoon, J. (2020) · Biosensors · doi:10.3390/bios10120193 · Cited by 130
Comparative Analysis of Radar and Lidar Technologies for Automotive Applications
Bilik, I. (2022) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2022.3162886 · Cited by 125
Tan, S. Y., & Taeihagh, A. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2020.101546 · Cited by 125
Developments in Modern GNSS and Its Impact on Autonomous Vehicle Architectures
Joubert, N., Reid, T., & Noble, F. (2020) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv47402.2020.9304840 · Cited by 105
Muzahid, A. J. M., Kamarulzaman, S. F., Rahman, M. A., Murad, S. A., Kamal, M. A. S., & Alenezi, A. H. (2023) · Scientific Reports · doi:10.1038/s41598-022-27026-9 · Cited by 96
Hasanujjaman, M., Chowdhury, M. Z., & Jang, Y. M. (2023) · Sensors · doi:10.3390/s23063335 · Cited by 110
Autonomous Vehicle Evaluation: A Comprehensive Survey on Modeling and Simulation Approaches
Alghodhaifi, H., & Lakshmanan, S. (2021) · IEEE Access · doi:10.1109/access.2021.3125620 · Cited by 89
A systematic literature review of the factors influencing the adoption of autonomous driving
Alawadhi, M., Al-Mazrouie, J. R., Kamil, M., & Khalil, K. A. (2020) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-020-00961-4 · Cited by 94
Song, R., & Li, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076164 · Cited by 86
A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles
Khan, S. K., Shiwakoti, N., & Stasinopoulos, P. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2021.106515 · Cited by 82
A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles
Yan, G., Jiang, C., Bai, Y. D., Yu, C., & Yu, C. (2022) · Processes · doi:10.3390/pr10040724 · Cited by 82
Cunneen, M., Mullins, M., Murphy, F., Shannon, D., Furxhi, I., & Ryan, C. (2019) · Cybernetics & Systems · doi:10.1080/01969722.2019.1660541 · Cited by 72
The Role of Physics-Based Simulators in Robotics
Liu, C. K., & Negruţ, D. (2020) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-072220-093055 · Cited by 74
Virtual Testing of Automated Driving Systems. A Survey on Validation Methods
Donà, R., & Ciuffo, B. (2022) · IEEE Access · doi:10.1109/access.2022.3153722 · Cited by 73
A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions
Matos, F., Bernardino, J., Duraes, J. A., & Cunha, J. C. (2024) · Sensors · doi:10.3390/s24165108 · Cited by 94
Perumal, P. S., Sujasree, M., Chavhan, S., Gupta, D. K., Mukthineni, V., Shimgekar, S. R., Khanna, A. K., & Fortino, G. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104406 · Cited by 73
A Review of Testing Object-Based Environment Perception for Safe Automated Driving
Hoss, M., Scholtes, M., & Eckstein, L. (2022) · Automotive Innovation · doi:10.1007/s42154-021-00172-y · Cited by 70
Urban rail transit obstacle detection based on Improved R-CNN
He, D., Ren, R., Li, K., Zou, Z., Ma, R., Qin, Y., & Yang, W. (2022) · Measurement · doi:10.1016/j.measurement.2022.111277 · Cited by 72
Sonko, S., Etukudoh, E. A., Ibekwe, K. I., Ilojianya, V. I., & Daudu, C. D. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.1.0258 · Cited by 70
Deep learning and control algorithms of direct perception for autonomous driving
Lee, D., Chen, K.-L., Liou, K.-H., Liu, C.-L., & Liu, J. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01827-9 · Cited by 66
de Gelder, E., Paardekooper, J., Saberi, A. K., Elrofai, H., den Camp, O. O., Kraines, S. B., Ploeg, J., & Schutter, B. D. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3144803 · Cited by 73
Vulnerable objects detection for autonomous driving: A review
Khatab, E., Onsy, A., Varley, M. R., & Abouelfarag, A. A. (2021) · Integration · doi:10.1016/j.vlsi.2021.01.002 · Cited by 68
Goelles, T., Schlager, B., & Muckenhuber, S. (2020) · Sensors · doi:10.3390/s20133662 · Cited by 63
Review on Vehicle Detection Technology for Unmanned Ground Vehicles
Liu, Q., Li, Z., Yuan, S., Zhu, Y., & Li, X. (2021) · Sensors · doi:10.3390/s21041354 · Cited by 68
Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review
Galvao, L. G., Abbod, M., Kalganova, T., Palade, V., & Huda, M. N. (2021) · Sensors · doi:10.3390/s21217267 · Cited by 59
Espineira, J. P., Robinson, J., Groenewald, J., Chan, P. H., & Donzella, V. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3059310 · Cited by 58
Malawade, A. V., Mortlock, T., & Faruque, M. A. A. (2022) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps54341.2022.00013 · Cited by 63
Bifulco, G. N., Coppola, A., Petrillo, A., & Santini, S. (2022) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2022.2124868 · Cited by 55
Thandavarayan, G., Sepulcre, M., & Gozálvez, J. (2020) · IEEE Access · doi:10.1109/access.2020.3035119 · Cited by 54
Kljaić, Z., Pavković, D., Cipek, M., Trstenjak, M., Mlinarić, T. J., & Nikšić, M. (2023) · Future Internet · doi:10.3390/fi15110347 · Cited by 52
Jia, D., Sun, J., Sharma, A., Zheng, Z., & Liu, B. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.102984 · Cited by 52
SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving
Munir, F., Azam, S., & Jeon, M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636353 · Cited by 51
Visibility of various road markings for machine vision
Burghardt, T. E., Popp, R., Helmreich, B., Reiter, T., Böhm, G., Pitterle, G., & Artmann, M. (2021) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2021.e00579 · Cited by 53
Automated drivability: Toward an assessment of the spatial deployment of level 4 automated vehicles
Soteropoulos, A., Mitteregger, M., Berger, M., & Zwirchmayr, J. (2020) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2020.03.024 · Cited by 50
A Review of 4IR/5IR Enabling Technologies and Their Linkage to Manufacturing Supply Chain
Fanoro, M., Božanić, M., & Sinha, S. (2021) · Technologies · doi:10.3390/technologies9040077 · Cited by 50
Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios
de Gelder, E., Elrofai, H., Saberi, A. K., Paardekooper, J., den Camp, O. O., & Schutter, B. D. (2021) · IEEE Access · doi:10.1109/access.2021.3136585 · Cited by 50
From Automation System to Autonomous System: An Architecture Perspective
Chen, H., Wen, Y., Zhu, M., Huang, Y., Xiao, C., Wei, T., & Hahn, A. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9060645 · Cited by 53
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity SimulationPreprint
Zhong, Z., Tang, Y., Zhou, Y. H., Neves, V. D. O., Liu, Y., & Ray, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.00964 · Cited by 72
Candela, E., Doustaly, O., Parada, L., Feng, F., Demiris, Y., & Angeloudis, P. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103923 · Cited by 49
3D object detection for autonomous driving: Methods, models, sensors, data, and challenges
Ghasemieh, A., & Kashef, R. F. (2022) · Transportation Engineering · doi:10.1016/j.treng.2022.100115 · Cited by 47
Critical scenario identification for realistic testing of autonomous driving systems
Song, Q., Tan, K., Runeson, P., & Persson, S. (2022) · Software Quality Journal · doi:10.1007/s11219-022-09604-2 · Cited by 44
Comparative analysis of range sensors for the robust autonomous navigation – a review
Singh, R., & Nagla, K. S. (2019) · Sensor Review · doi:10.1108/sr-01-2019-0029 · Cited by 44
Berrada, J., Mouhoubi, I., & Christoforou, Z. (2020) · Research in Transportation Economics · doi:10.1016/j.retrec.2020.100902 · Cited by 44
Zhang, T., Liu, H., Wang, W., & Wang, X. (2024) · Electronics · doi:10.3390/electronics13173486 · Cited by 44
Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022) · Mathematics · doi:10.3390/math10183233 · Cited by 46
Abbasi, S., & Rahmani, A. M. (2023) · Information · doi:10.3390/info14100555 · Cited by 40
Development of an Energy Efficient and Cost Effective Autonomous Vehicle Research Platform
Brown, N. E., Rojas, J. F., Goberville, N. A., Alzu’bi, H., Alrousan, Q., Wang, C. R., Huff, S., Rios-Torres, J., Ekti, A. R., LaClair, T. J., et al. (2022) · Sensors · doi:10.3390/s22165999 · Cited by 40
Neuromorphic Skin Based on Emerging Artificial Synapses
Lee, Y., Oh, J. Y., & Lee, T. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202200193 · Cited by 40
Arrouch, I., Ahmad, N. S., Goh, P., & Mohamad–Saleh, J. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.04.041 · Cited by 38
Autonomous Vehicles in Intelligent Transportation Systems
Aldakkhelallah, A. A. A., & Simić, M. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3264-8_18 · Cited by 37
Magosi, Z. F., Li, H., Rosenberger, P., Wan, L., & Eichberger, A. (2022) · Sensors · doi:10.3390/s22155693 · Cited by 37
Chen, Y., Chen, S., Xiao, T., Zhang, S., Hou, Q., & Zheng, N. (2020) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv47402.2020.9304658 · Cited by 35
Navarro, P., Miller, L., Rosique, F., Fernández-Isla, C., & Gila-Navarro, A. (2021) · Electronics · doi:10.3390/electronics10111266 · Cited by 37
Self-citationGkoumas, K., Gkoktsi, K., Bono, F., Galassi, M. C., & Tirelli, D. (2021) · Infrastructures · doi:10.3390/infrastructures6030043 · Cited by 36
An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications
Gautam, A., He, Y., & Lin, X. (2024) · SAE International journal of vehicle dynamics, stability, and NVH · doi:10.4271/10-08-02-0011 · Cited by 38
Multilevel Data and Decision Fusion Using Heterogeneous Sensory Data for Autonomous Vehicles
Ignatious, H. A., El‐Sayed, H., & Kulkarni, P. (2023) · Remote Sensing · doi:10.3390/rs15092256 · Cited by 32
Toward a Commonly Shared Public Policy Perspective for Analyzing Risk Coping Strategies
Li, Y., Taeihagh, A., de Jong, M., & Klinke, A. (2020) · Risk Analysis · doi:10.1111/risa.13505 · Cited by 32
Yang, S. M., & Lin, Y.-Y. (2021) · Sensors · doi:10.3390/s21062244 · Cited by 32
Ding, S., Abdel-Aty, M. A., Barbour, N., Wang, D., Wang, Z., & Zheng, O. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107692 · Cited by 31
Aledhari, M., Razzak, R., Rahouti, M., Yazdinejad, A., Parizi, R. M., Qolomany, B., Guizani, M., Qadir, J., & Al‐Fuqaha, A. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104352 · Cited by 31
Nowakowski, M., & Kurylo, J. (2023) · Applied Sciences · doi:10.3390/app13084892 · Cited by 31
Precise Timing for Vehicle Navigation in the Smart City: An Overview
Neil, J., Cosart, L., & Zampetti, G. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900596 · Cited by 30
Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis
Azam, M., Hassan, S. A., & Puan, O. C. (2022) · Sustainability · doi:10.3390/su141710743 · Cited by 30
Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles—A Survey
Ignatious, H. A., El‐Sayed, H., Khan, M. A., & Mokhtar, B. M. (2022) · Sensors · doi:10.3390/s23084075 · Cited by 34
Ultra-Sonic Sensor based Object Detection for Autonomous Vehicles
Nesti, T., Boddana, S., & Yaman, B. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00026 · Cited by 30
Governance of the Risks of Ridesharing in Southeast Asia: An In-Depth Analysis
Icasiano, C. D. A., & Taeihagh, A. (2021) · Sustainability · doi:10.3390/su13116474 · Cited by 29
TrustFSDV: Framework for Building and Maintaining Trust in Self-Driving Vehicles
Kuru, K. (2022) · IEEE Access · doi:10.1109/access.2022.3196941 · Cited by 40
Xu, Z., & Zheng, N. (2024) · Multimodal Transportation · doi:10.1016/j.multra.2024.100133 · Cited by 28
PSE-Match: A Viewpoint-Free Place Recognition Method With Parallel Semantic Embedding
Yin, P., Xu, L., Feng, Z., Egorov, A., & Li, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3102429 · Cited by 28
Xu, Z., Fang, Y., Zheng, N., & Vu, H. L. (2022) · Journal of Intelligent and Connected Vehicles · doi:10.1108/jicv-05-2022-0017 · Cited by 30
Niskanen, I., Immonen, M., Makkonen, T., Keränen, P., Tyni, P., Hallman, L. W., Hiltunen, M., Kolli, T., Louhisalmi, Y., Kostamovaara, J., et al. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103112 · Cited by 27
A comprehensive review of path planning algorithms for autonomous navigation
Venu, S., & Gurusamy, M. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107750 · Cited by 31
Oladele, D. A., Markus, E. D., & Abu-Mahfouz, A. M. I. (2021) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/29610 · Cited by 29
Digital Twin-enabled Reinforcement Learning for End-to-end Autonomous Driving
Wu, J., Huang, Z., Hang, P., Huang, C., de Boer, N., & Lv, C. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540179 · Cited by 26
Metadata
- Authors
- Rosique, F., Navarro, P. J., Fernández, C., & Padilla, A.
- Affiliations
- Universidad Politécnica de Cartagena (ES)
- Corresponding author
- Rosique, Francisca
- Navarro, Pedro J.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-02-05
- Volume
- 19
- Issue
- 3
- Pages
- 648
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6387009
- OpenAlex
- W2912493354
- Cited by
- 469
- References
- 63
- Citations by year
- 2026: 34 · 2025: 49 · 2024: 80 · 2023: 96 · 2022: 71 · 2021: 74 · 2020: 47 · 2019: 17
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Digital Repository (Polytechnic University of Cartagena) (submittedVersion, cc-by-nc-nd)
- Funders
- Dirección General de Tráfico (SPIP2017-02286)
- Ministerio de Economía y Competitividad (BFU2017-88300-C2-2-R)
- Fundación Séneca (19895/GERM/15)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Keywords
- autonomous vehicles, perception systems, vehicle simulator, sensor comparison, ultrasonic sensors, radar, LiDAR, camera, inertial measurement unit, GNSS, RTK, game engines, model-driven engineering, autonomous vehicle regulation
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
