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
Items 401–469 of 469
Diploma Thesis Self-driving (Autonomous) Car Simulation in Ros SystemPreprint
Jawad, Z. N. (2023) · Research Square · doi:10.21203/rs.3.rs-3649061/v1
Liu, Y., Dai, M., Chen, C., Du, J., & Zhao, S. (2023) · International Conference on Control and Intelligent Robotics (ICCIR) · doi:10.1117/12.3010656
Impact of Misperception on Emergence of Risk in Platoon of Autonomous Vehicles
Amini, A., Liu, G., Pandey, V. K., & Motee, N. (2023) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc49753.2023.10383863
Intruder Vehicle Detection During a Platoon Joining Maneuver
Gharbi, H., Jemili, I., & Mabrouk, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-52823-1_5
Hur, B., Conway, R., González, A., Rodriguez, A., & Bunyavong, J. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--41689
Li, C., Wang, Y., Li, K., Fan, Q., Shen, Y., & Ji, Y. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422311
Investigation of AV Operational Issues Using Simulation Equipment
Saka, Z. A., & Labi, S. (2024) · doi:10.5703/1288284317737
Masterson, A., Patil, P., Brown, N. E., Asher, Z. D., Rojas, J. F., Siems-Anderson, A. R., Walker, C. L., & Rabinowitz, A. I. (2024) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2024-01-2044
Development, Simulation and Visualization of Autonomous Farm Vehicles
Reddy, K. K., & Rao, M. S. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.58489
Smart Routing Bus Transportation in Rural Terminals
Geetha, B. G., Sandhiya, G., Shalini, A., & Sharmila, M. (2024) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla59606.2024.10531551
Li, Y., & Taeihagh, A. (2024) · Public Organization Review · doi:10.1007/s11115-024-00785-x
UWB sensor performance in UGV 'Follow me' applications with obstacle interference
Pavlović, R., Mitričević, N., & Savić, S. (2024) · International Scientific Conference on Defensive Technologies (OTEX) · doi:10.5937/oteh24055p
A Comprehensive Systematic Scoping Review of Self-Driving Vehicle Models
Ajao, Q. M., Sodiq, O. O., & Sadeeq, L. (2024) · African Journal of Advances in Science and Technology Research · doi:10.62154/ajastr.2024.017.010458
Hybrid Testbeds with a Focus on Sensors in Space Robotics Applications
Thieling, J., & Roßmann, J. (2024) · Springer eBooks · doi:10.1007/978-3-031-39214-6_14
Tulsyan, A., Bhardwaj, A., Shukla, P., Gautam, P., & Singh, T. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem40459
A Path-Tracking Controller Design For Distributed Drive Electric Vehicles with All-Wheel Steering
Wang, Y., Wang, L., Fang, R., Zhuang, W., Wang, K., & Yin, G. (2024) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci63518.2024.10830147
A Survey of Validation and Verification Methods for AI-Based Vehicle Functions
Aslandere, T., & Durak, U. (2025) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-08-04-0031
BEAM: Bilevel Evaluation and Analysis of Multi-Object-Tracking Under Latency
Bauer, E., Yin, Y., Ayeb, M., Krause, C., & Brabetz, L. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10920222
Bio Cognitive Mesh: Harnessing Swarm Intelligence for Self-Organizing AI at the Network Edge
Gupta, S. (2025) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr62650.2025.11020117
Anticipatory adaptive cruise control (AACC) for enhanced performance in mixed traffic flow
Ebadi, O., Heshami, S., & Kattan, L. (2025) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2024-0447
navTD3: An End-to-End Learning Approach for Indoor Navigation
Mutlu, F. B., Cicek, D. C., Guler, R. B., & Kozat, S. S. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04329-8
Convolved Thinned Antenna Arrays for Close Target Separation
Hamida, A., Saif, M., Li, J., & Valaee, S. (2025) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc66079.2025.11143509
Lodhi, S. S., Kumar, N., & Pandey, P. K. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3615629
Optical–Structural Optimization for Condensation Suppression in Automotive Camera Modules
Zhang, K. H., Xu, Y., Xu, S., Lin, X., Zhou, J., Liu, Z., Li, Y., & Wei, H. (2025) · Sensors · doi:10.3390/s25216515
Gaddam, M. K., Kapoor, S., Vedula, J., Manu, M., Usmani, M. A., & Polvanov, S. (2025) · International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon65200.2025.11210718
Towards Virtual Testing of Perception in the Railway Domain
Wild, M., Becker, J. S., Buinoschi-Tirpescu, A., & Möhlmann, E. (2025) · IEEE International Automated Vehicle Validation Conference (IAVVC) · doi:10.1109/iavvc61942.2025.11219512 · Cited by 1
Fülöp, T., & Katona, J. (2025) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-025-00621-5 · Cited by 1
CARLA Hardware-in-the-Loop Simulation Environment for Autonomous Driving
Cardozo, S. M., & Hlaváč, V. (2025) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-09-02-0015
Wang, X., Li, C., Li, Y., Sun, L., Xu, C., Hou, X., Wang, J., Guo, M., & Zhao, Y. (2025) · IEEE Transactions on Computers · doi:10.1109/tc.2025.3644184
A Survey on Enhancing Autonomous Vehicle Safety through Fault Tree Analysis and SOTIF Integration
Dai, L., & Kantarcı, B. (2026) · ACM Journal on Autonomous Transportation Systems · doi:10.1145/3787221
Development of a Customizable and Easy-to-use Driving VR Simulator Based on 3D City Model
Sato, K., Cong, R., & Jin, Q. (2025) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/cyberscitech68397.2025.00086
Pohan, M. A. R., Warlina, L., Utama, J., Herdiana, B., & Abdurashidovna, S. D. (2025) · NED University Journal of Research · doi:10.35453/nedjr-incitest013-2025
Obstacle Detecting Self-driving Car with Email Alert and Live Monitoring System
Banerjee, S., Singh, G., Rai, B., Akshaya, Y. E. A., Yadav, S., & Paul, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15398-2_35
A Systematic Analysis on Blockchain Supported Intelligent Vehicular Networks
Garg, D., Bansal, N., & Dash, S. (2025) · International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT) · doi:10.1109/icec2nt65402.2025.11380093
Predicting Technological Trends and Effects Enabling Large-Scale Supply Drones
Joyce, K. J., Hargreaves, M., Amos, J., Arnold, M., Austin, M., Le, B., Joiner, K. F., Daria, V. R., & Young, J. (2026) · Technologies · doi:10.3390/technologies14030155
Toward Optimal Perception Sensor Configuration for Vehicle Trajectory Prediction
Gamage, A. G., & Donzella, V. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3668975
Design and Development of an Autonomous Self-Driving Robot Using Deep Learning Technique
Alshbatat, A. I. N., Mahasneh, B. Z., & Awawdeh, M. (2025) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset66891.2025.11427999
Mujagoni, B., Tigadi, A., & Mahajan, P. (2025) · IEEE Global Conference on Wireless Computing and Networking (GCWCN) · doi:10.1109/gcwcn66157.2025.11448505
Mimouna, A. (2021) · theses.fr (ABES) · doi:10.70675/b4a6b801zab88z421cz82a3z481c405a8d90 · Cited by 1
Night-Time Pedestrian Detection Using Millimetre-Wave Radar and Visual Data
Sagar, V. V., Krishna, M. V. N. S., Satyasri, P. D. N., Sony, V., & Nalluri, N. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815324488126040005
Karimi, A., Mao, S., Lotto, G., Lee, J., & Bassani, M. (2026) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2026.101986 · Cited by 1
C, A. R. K., C, S. K., Reddy, Y. S., Reddy, N. M., Koshy, S. S., & Machina, V. S. P. (2026) · Contemporary Computing Innovations Conference (CCIC) · doi:10.1109/ccic68129.2026.11486099
Qiu, Q., Xu, Z., Gao, Y. L., Jiang, Y., 许, 志., Liu, C., Tian, B., Li, X., Qu, X., Wang, J., et al. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105737
Multi-Source Fusion Positioning Revisited by Drawing on Human Thinking Process
Chen, J., Li, Q., Ke, D., Liao, J., & Zhang, D. G. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3697508
Challenges, Advances and Future Prospects of Vehicle Detection in Snow
Reza, I., Khanal, N., Zlatkovic, M., Muknahallipatna, S., & Ksaibati, K. (2026) · AI Computer Science and Robotics Technology · doi:10.5772/acrt20250135
Xie, D.-T., Zhao, Y., Bi, S., Zhao, Y., Lu, H., Wang, N., & He, X. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261454019
Design and Implementation of Multi-Sensor Perception System for Autonomous Vehicle
Garmoum, S., Mahri, M. E., Akaabour, K., & Jarou, T. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538501
LLM-driven automated road network generation and alignment for high-fidelity traffic co-simulation
Shi, Y., Qiao, B., Chen, Q., Yang, Z., Xu, Y., Wu, T., Liu, Z., & Liu, S. (2026) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2026.131759
Digital Twin for Safety Validation and Crash Scenario Simulations
Aarthi, S., Ravikumar, R. N., Turaev, D., Ashirova, A. I., Urozboev, K., & Naveena, N. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7785-8.ch004
Event Detection in Hybrid Simulation
Biswas, P., Kühn, T., Mahajan, D., Zech, P., & Mirelmi, P. (2026) · ACM/IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation (PADS) · doi:10.1145/3806789.3810254
Risk‐Aware Motorcycle Interaction in Mixed Traffic Flow via Deep Reinforcement Learning
Wei, Y., Ma, K., Chen, G. L., & Wong, S. (2026) · Computer Animation and Virtual Worlds · doi:10.1002/cav.70161
Anti, N. Y. D., Frimpong, S., & Raza, M. A. (2026) · Sensors · doi:10.3390/s26134258
Uzer, E. H., & Haniš, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-33182-3_9
Shit, R. C. (2026) · Computer Communications · doi:10.1016/j.comcom.2026.108654
Fast and Scalable LiDAR Data Generation for Autonomous Driving Simulation Without Raycasting
Shahan, I. N., Nabil, A.-M. K., & Kusari, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37092-1_16
Data Leakage in Automotive Perception: Practitioners' Insights
Babu, M. A. A., Pandey, S. K., Durisic, D., Bálint, A. F., & Staron, M. (2026) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3793653.3793775
Ton, Q. T., Vu, T. H., Vũ, T. N., & Le, T. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116499
Li, J., Wu, P., Yang, X., Li, Z., Pengwen, X., Yang, L., Chi, H., Xie, X., Zeng, C., Cen, W., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.116390
Modeling Reliable Autonomous Driving with CARLA-Based Benchmark for Visual SLAM
Alazzani, M., & Han, K. (2026) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins69995.2026.11715561
Digital twin technologies for automated vehicles
Perumal, H., Mayakrishnan, M., Sukkasamy, S., Sahayaraj, A. F., & Ramakrishnan, S. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-43962-9.00006-1
Świadek, A., Warguła, Ł., Wiatrowska, W., & Wieczorek, B. (2026) · Energies · doi:10.3390/en19204934
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