A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research

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

Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?

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

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

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

Design, Implementation and Experimental Investigation of a Pedestrian Street Crossing Assistance System Based on Visible Light Communications

Căilean, A.-M., Beguni, C., Avătămăniței, S.-A., Dimian, M., & Popa, V. (2022) · Sensors · doi:10.3390/s22155481 · Cited by 26

Scalable cooperative perception for connected and automated driving

Thandavarayan, G., Sepulcre, M., Gozálvez, J., & Coll-Perales, B. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103655 · Cited by 26

An Exploratory Analysis of Temporal and Spatial Patterns of Autonomous Vehicle Collisions

Patel, R. K., Channamallu, S. S., Khan, M. A., Kermanshachi, S., & Pamidimukkala, A. (2024) · Public Works Management & Policy · doi:10.1177/1087724x231217677 · Cited by 30

Impacts of Different Driving Automation Levels on Highway Geometric Design from the Perspective of Trucks

Wang, S., Yu, B., Ma, Y., Liu, J., & Zhou, W. (2021) · Journal of Advanced Transportation · doi:10.1155/2021/5541878 · Cited by 23

Open-Source Integrated Simulation Framework for Cooperative Autonomous Vehicles

Anagnostopoulos, C., Koulamas, C., Lalos, A. S., & Stylios, C. D. (2022) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco55406.2022.9797115 · Cited by 24

Two is better than one: digital siblings to improve autonomous driving testing

Biagiola, M., Stocco, A., Riccio, V., & Tonella, P. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10458-4 · Cited by 24

In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data

Shokravi, H., Vafaei, M., Samali, B., & Bakhary, N. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13180 · Cited by 27

Phoenix: Towards Designing and Developing a Human Assistant Rover

Zaman, A., Majib, M. S., Tanjim, S. A., Siddique, S. M. A., Ashraf, F., Islam, S., Morshed, A. H. M. M., Shahid, S. T., Hasan, I., Samir, O., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3170908 · Cited by 23

MCRB on DOA Estimation for Automotive MIMO Radar in the Presence of Multipath

Levy-Israel, M., Bilik, I., & Tabrikian, J. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3285861 · Cited by 23

Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison

Kozłowski, M., Racewicz, S., & Wierzbicki, S. (2024) · Applied Sciences · doi:10.3390/app14188150 · Cited by 26

Human-centred design of next generation transportation infrastructure with connected and automated vehicles: a system-of-systems perspective

Feng, Y., Chen, Y., Zhang, J., Tian, C., Ren, R., Han, T., & Proctor, R. W. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2023.2182003 · Cited by 24

Categorized review of drive simulators and driver behavior analysis focusing on ACT-R architecture in autonomous vehicles

Cina, M., & Rad, A. B. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103044 · Cited by 21

Super-Resolution Radar Imaging With Sparse Arrays Using a Deep Neural Network Trained With Enhanced Virtual Data

Schüßler, C., Hoffmann, M., & Vossiek, M. (2023) · IEEE Journal of Microwaves · doi:10.1109/jmw.2023.3285610 · Cited by 21

Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios

Song, Q., Engström, E., & Runeson, P. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3640334 · Cited by 21

Small-scale self-driving cars: A systematic literature review

Caleffi, F., Rodrigues, L. D. S., Stamboroski, J. D. S., & Pereira, B. M. (2024) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2023.09.005 · Cited by 25

Wavefield Networked Sensing: Principles, Algorithms, and Applications

Manzoni, M., Tagliaferri, D., Tebaldini, S., Mizmizi, M., Guarnieri, A. M., Prati, C. M., & Spagnolini, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3521359 · Cited by 21

Towards real-time wide-field fluorescence lifetime imaging of 5-ALA labeled brain tumors with multi-tap CMOS cameras

Reichert, D., Erkkilä, M. T., Holst, G., Hecker‐Denschlag, N., Wilzbach, M., Hauger, C. G., Drexler, W., Gesperger, J., Kiesel, B., Roetzer-Pejrimovsky, T., et al. (2020) · Biomedical Optics Express · doi:10.1364/boe.382817 · Cited by 20

Pixel-Wise Motion Deblurring of Thermal Videos

Ramanagopal, M. S., Zhang, Z., Vasudevan, R., & Johnson‐Roberson, M. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.022 · Cited by 20

Origami inspired phononic structure with metamaterial inclusions for tunable angular wave steering

Hathcock, M., Popa, B.-I., & Wang, K. W. (2021) · Journal of Applied Physics · doi:10.1063/5.0041503 · Cited by 20

Modeling Cameras for Autonomous Vehicle and Robot Simulation: An Overview

Elmquist, A., & Negruţ, D. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3118952 · Cited by 21

CARLA Simulator-Based Evaluation Framework Development of Lane Detection Accuracy Performance Under Sensor Blockage Caused by Heavy Rain for Autonomous Vehicle

Jeon, H., Kim, Y., Choi, M., Park, D., Son, S., Lee, J., Choi, G., & Lim, Y. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3192632 · Cited by 20

A Car-Following Model Considering Missing Data Based on TransGAN Networks

Xu, D., Gao, G., Qiu, Q., Shang, X., & Li, H. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3270336 · Cited by 20

A virtual reality experiment to study pedestrian perception of future street scenarios

Sánchez–Vaquerizo, J. A., Hausladen, C. I., Mahajan, S., Matter, M., Siebenmann, M., van Eggermond, M. A. B., & Helbing, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-55073-x · Cited by 20

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features

Silva, I., da Silva, H. D. M., Botelho, F., & Pendão, C. (2024) · Sensors · doi:10.3390/s24185880 · Cited by 20

Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles

Zofka, M. R., Töttel, L., Zipfl, M., Heinrich, M., Fleck, T., Schulz, P., & Zöllner, J. M. (2020) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi49285.2020.9235238 · Cited by 20

Testing Deep Learning-based Visual Perception for Automated Driving

Abrecht, S., Gauerhof, L., Gladisch, C., Groh, K., Heinzemann, C., & Woehrle, M. (2021) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3450356 · Cited by 22

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Khatab, E., Onsy, A., & Abouelfarag, A. A. (2022) · Sensors · doi:10.3390/s22041663 · Cited by 21

Integrating Connected and Automated Shuttles With Other Mobility Systems: Challenges and Future Directions

Chaalal, E., Guerlain, C., Pardo, E., & Faye, S. (2023) · IEEE Access · doi:10.1109/access.2023.3294110 · Cited by 19

CARLA-Autoware-Bridge: Facilitating Autonomous Driving Research with a Unified Framework for Simulation and Module Development

Kaljavesi, G., Kerbl, T., Betz, T., Mitkovskii, K., & Diermeyer, F. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588623 · Cited by 19

Sensors for autonomous vehicles

Dong, W. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99545-0.00021-x · Cited by 19

Forecasting of in situ electron energy loss spectroscopy

Lewis, N. R., Jin, Y., Tang, X., Shah, V., Doty, C., Matthews, B. E., Akers, S. M., & Spurgeon, S. R. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00940-2 · Cited by 20

A Survey on Autonomous Driving System Simulators

Zhou, J., Zhang, Y., Guo, S., & Guo, Y. P. (2022) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew55968.2022.00084 · Cited by 18

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Lazăr, R.-G., Păucă, O., Maxim, A., & Caruntu, C. F. (2023) · Sensors · doi:10.3390/s23177576 · Cited by 20

Effective Range Assessment of Lidar Imaging Systems for Autonomous Vehicles Under Adverse Weather Conditions With Stationary Vehicles

Abdo, J., Hamblin, S., & Chen, G. (2021) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4052228 · Cited by 18

Sensor Fusion Method for Object Detection and Distance Estimation in Assisted Driving Applications

Favelli, S., Xie, M., & Tonoli, A. (2024) · Sensors · doi:10.3390/s24247895 · Cited by 21

Plant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications

Shoaib, M., Khan, S. H., Abdelhameed, H., & Qahmash, A. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1670593 · Cited by 22

FecNet: A Feature Enhancement and Cascade Network for Object Detection Using Roadside LiDAR

Gong, Z., Wang, Z., Yu, G., Liu, W., Yang, S., & Zhou, B. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3304623 · Cited by 17

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network

Ogunrinde, I. O., & Bernadin, S. (2024) · Sensors · doi:10.3390/s24144692 · Cited by 19

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

Zhang, S., & Tak, T.-O. (2023) · IET Intelligent Transport Systems · doi:10.1049/itr2.12466 · Cited by 16

Humanizing autonomous vehicle driving: Understanding, modeling and impact assessment

Orfanou, F. P., Vlahogianni, E. I., Yannis, G., & Mitsakis, E. (2022) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2022.04.008 · Cited by 15

DFA-SAT: Dynamic Feature Abstraction with Self-Attention-Based 3D Object Detection for Autonomous Driving

Mushtaq, H., Deng, X., Ali, M., Hayat, B., & Sherazi, H. H. R. (2023) · Sustainability · doi:10.3390/su151813667 · Cited by 15

Multi-agent simulation of autonomous industrial vehicle fleets: Towards dynamic task allocation in V2X cooperation mode

Grosset, J., Fougères, A.-J., Djoko‐Kouam, M., & Bonnin, J. (2024) · Integrated Computer-Aided Engineering · doi:10.3233/ica-240735 · Cited by 15

Autonomous Forklifts: State of the Art—Exploring Perception, Scanning Technologies and Functional Systems—A Comprehensive Review

Fraifer, M., Coleman, J., Maguire, J., Trslić, P., Dooly, G., & Toal, D. (2025) · Electronics · doi:10.3390/electronics14010153 · Cited by 15

Worsening Perception: Real-Time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

Fursa, I., Fandi, E., Muşat, V., Culley, J., Gil, E., Teeti, I., Bilous, L., Sluis, I. V., Rast, A. D., & Bradley, A. (2022) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-05-01-0008 · Cited by 21

University students’ perspectives on autonomous vehicle adoption: Adelaide case study

Soltani, A., Ananda, D., & Rith, M. (2021) · Case Studies on Transport Policy · doi:10.1016/j.cstp.2021.11.004 · Cited by 15

Human awareness versus Autonomous Vehicles view: comparison of reaction times during emergencies

Rydzewski, A., & Czarnul, P. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575602 · Cited by 17

PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles

Piazzoni, A., Cherian, J., Dauwels, J., & Chau, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3311633 · Cited by 14

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Lee, P., Θεοτοκάτος, Γ., & Boulougouris, E. K. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12040557 · Cited by 15

Non-Line-of-Sight Vehicle Localization Based on Sound

Jeon, M., Cho, J.-K., Kim, H.-Y., Park, B., Seo, S., & Kim, S.-W. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3510582 · Cited by 14

Self-Driving Car Safety Quantification via Component-Level Analysis

Vaicenavičius, J., Wiklund, T., Grigaite, A., Kalkauskas, A., Vysniauskas, I., & Keen, S. D. (2021) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-04-01-0004 · Cited by 14

A Gamified Simulator and Physical Platform for Self-Driving Algorithm Training and Validation

Παππάς, Γ., Siegel, J. E., Politopoulos, K., & Sun, Y. (2021) · Electronics · doi:10.3390/electronics10091112 · Cited by 15

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

Rosique, F., Navarro, P., Miller, L., & Salas, E. (2023) · Sensors · doi:10.3390/s23042009 · Cited by 14

Machine learning testing in an ADAS case study using simulation‐integrated bio‐inspired search‐based testing

Moghadam, M. H., Borg, M., Saadatmand, M., Mousavirad, S. J., Bohlin, M., & Lisper, B. (2023) · Journal of Software Evolution and Process · doi:10.1002/smr.2591 · Cited by 14

AVDM: A hierarchical command-and-control system architecture for cooperative autonomous vehicles in highways scenario using microscopic simulations

Braud, T., Ivanchev, J., Deboeser, C., Knoll, A., Eckhoff, D., & Sangiovanni-Vincentelli, A. L. (2021) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-021-09499-6 · Cited by 15

How certain are we that our automated driving system is safe?

de Gelder, E., & den Camp, O. O. (2023) · Traffic Injury Prevention · doi:10.1080/15389588.2023.2186733 · Cited by 13

CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous drivingPreprint

Osiński, B., Miłoś, P., Jakubowski, A., Zięcina, P., Martyniak, M., Galias, C., Breuer, A., Homoceanu, S., & Michalewski, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11329 · Cited by 21

Digital twins-enabled game theoretical models and techniques for metaverse Connected and Autonomous Vehicles: A survey

Aslam, A. M., Chaudhary, R., Bhardwaj, A., Kumar, N., & Buyya, R. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104138 · Cited by 17

Machine Vision for Construction Equipment by Transfer Learning with Scale Models

Borngrund, C., Bodin, U., & Sandin, F. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207577 · Cited by 12

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk, P., Komorkiewicz, M., Skruch, P., & Szelest, M. (2022) · IEEE Access · doi:10.1109/access.2022.3145192 · Cited by 13

A cooperative approach to avoiding obstacles and collisions between autonomous industrial vehicles in a simulation platform

Grosset, J., Ndao, A., Fougères, A.-J., Djoko‐Kouam, M., Couturier, C., & Bonnin, J. (2022) · Integrated Computer-Aided Engineering · doi:10.3233/ica-220694 · Cited by 12

A Continuous Camera Placement Optimization Model for Surround View

Puligandla, V. A., & Lončarić, S. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3299199 · Cited by 12

Application of Automobile Modelling Optimization Design Based on Virtual Reality Technology

Wu, W., Sivaparthipan, C. B., & Sanz-Prieto, I. (2021) · Journal of Interconnection Networks · doi:10.1142/s0219265921430076 · Cited by 12

CAOM: Change-aware online 3D mapping with heterogeneous multi-beam and push-broom LiDAR point clouds

Cong, Y., Chen, C., Yang, B., Liang, F., Ma, R., & Fei, Z. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.017 · Cited by 11

Automatic Test and Evaluation of Autonomous Systems

Karimoddini, A., Khan, M. A., Gebreyohannes, S., Heiges, M., Trewhitt, E. B., & Homaifar, A. (2022) · IEEE Access · doi:10.1109/access.2022.3183145 · Cited by 11

In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors

Kettelgerdes, M., & Elger, G. (2023) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2023.3234999 · Cited by 11

A Cooperative Control Algorithm for Line and Predecessor Following Platoons Subject to Unreliable Distance Measurements

Escobar, C., Vargas, F. J., Peters, A. A., & Carvajal, G. (2023) · Mathematics · doi:10.3390/math11040801 · Cited by 11

Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems

Sauerbeck, F., Huch, S., Fent, F., Karle, P., Kulmer, D., & Betz, J. (2023) · IEEE Access · doi:10.1109/access.2023.3272750 · Cited by 14

Generating Graph-Inspired Descriptors by Merging Ground-Level and Satellite Data for Robot Localization

Balaska, V. E., Bampis, L., Katsavounis, S., & Gasteratos, A. C. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2022.2073701 · Cited by 10

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

Magosi, Z. F., & Eichberger, A. (2023) · Sensors · doi:10.3390/s23063227 · Cited by 10

Artificial Intelligence-Assisted Robustness of Optoelectronics for Automated Driving: A Review

Nacpil, E. J. C., Han, J., & Jeon, I. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3309290 · Cited by 10

SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions

El-Shair, Z. A., Abu-raddaha, A., Cofield, A., Alawneh, H., Aladem, M., Hamzeh, Y., & Rawashdeh, S. A. (2024) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon61878.2024.10670659 · Cited by 13

A Universal Simulation Framework of Shipborne Inertial Sensors Based on the Ship Motion Model and Robot Operating System

Jing, Q., Wang, H., Hu, B., Liu, X., & Yin, Y. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9080900 · Cited by 10

Implementation of a Perception Module for Smart Mobility Applications in Eclipse MOSAIC

Protzmann, R., Schrab, K., Schweppenhäuser, M., & Radusch, I. (2022) · SUMO Conference Proceedings · doi:10.52825/scp.v3i.123 · Cited by 10

Feasible and stressful trajectory generation for mobile robots

Hildebrandt, C., Elbaum, S. G., Bezzo, N., & Dwyer, M. B. (2020) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3395363.3397387 · Cited by 10

Critical Scenario Identification for Realistic Testing of Autonomous Driving SystemsPreprint

Song, Q., Tan, K., Runeson, P., & Persson, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1280095/v1 · Cited by 10

Towards a Review on Simulated ADAS/AD Testing

Köroglu, Y., & Wotawa, F. (2023) · doi:10.1109/ast58925.2023.00015 · Cited by 11

Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey

Maheshwari, H., Yang, L., & Pazzi, R. W. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3589208 · Cited by 14

Vision technologies in autonomous vehicles: progress, methodologies, and key challenges

Kumar, H., Mamoria, P., & Dewangan, D. K. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02912-3 · Cited by 12

Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques

Venugopala, S. H. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2232/1/012015 · Cited by 9

Challenges in Automotive MIMO Radar Calibration in Anechoic Chamber

Raphaeli, D., & Bilik, I. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3272875 · Cited by 9

Sequence-coded coherent laser ranging with high detection sensitivity

Shemer, K., Bashan, G., Diamandi, H. H., London, Y., Raanan, T., Israelashvili, Y., Charni, A., Cohen, I., Bergman, A., Levanon, N., et al. (2020) · OSA Continuum · doi:10.1364/osac.386816 · Cited by 9

A Virtual Reality-based Platform to Validate HMI Design for Increasing User’s Trust in Autonomous Vehicle

Shi, Y., Azzolin, N., Picardi, A., Zhu, T., Bordegoni, M., & Caruso, G. (2020) · Computer-Aided Design and Applications · doi:10.14733/cadaps.2021.502-518 · Cited by 8

Image Radar-based Traffic Surveillance System: An all-weather sensor as intelligent transportation infrastructure component

Du, Y. L., Man, K. L., & Lim, E. G. (2020) · International SoC Design Conference · doi:10.1109/isocc50952.2020.9333124 · Cited by 9

An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Song, Q., Tan, K., Runeson, P., & Persson, S. (2021) · IEEE International Conference on Artificial Intelligence Testing (AITest) · doi:10.1109/aitest52744.2021.00024 · Cited by 9

A Two-Layered Approach for the Validation of an Operational Autonomous Shuttle

Malayjerdi, M., Goss, Q., Akbaş, M. İ., Sell, R., & Bellone, M. (2023) · IEEE Access · doi:10.1109/access.2023.3306602 · Cited by 9

Scalable Sensor Models and Simulation Methods for Seamless Transitions Within System Development: From First Digital Prototype to Final Real System

Thieling, J., & Rosmann, J. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3006739 · Cited by 9

Optimizing Delegation in Collaborative Human-AI Hybrid Teams

Fuchs, A., Passarella, A., & Conti, M. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3687130 · Cited by 8

A Co-Simulation Platform for V2X-Based Cooperative Driving Automation Systems

Jooriah, M., Datsenko, D., Almeida, J., Sousa, A., Silva, J. M. N., & Ferreira, J. (2024) · IEEE Vehicular Networking Conference (VNC) · doi:10.1109/vnc61989.2024.10575990 · Cited by 8

Visual Odometry/Inertial Integration for Enhanced Land Vehicle Navigation in GNSS Denied Environment

Salib, A., Moussa, M., Moussa, A., & El‐Sheimy, N. (2020) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2020-fall49728.2020.9348698 · Cited by 8

Emerging opportunities for wildlife conservation with sustainable autonomous transportation

Silva, I., & Calabrese, J. M. (2023) · Frontiers in Ecology and the Environment · doi:10.1002/fee.2697 · Cited by 8

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

Alkashto, H., & Elewi, A. (2024) · Turkish Journal of Engineering · doi:10.31127/tuje.1366248 · Cited by 8

Integrity for autonomous vehicles and towards a novel alert limit determination method

Meng, Q., & Hsu, L. (2020) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/0954407020965760 · Cited by 8

CiThruS2: Open-source Photorealistic 3D Framework for Driving and Traffic Simulation in Real Time

Gałązka, E., Niemirepo, T. T., & Vanne, J. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564751 · Cited by 8

Channel Boosting Feature Ensemble for Radar-based Object Detection

Azam, S., Munir, F., & Jeon, M. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575600 · Cited by 8

Making autonomous vehicle systems human-like: lessons learned from accident experiences in traffic

Lee, C. K. H., & Wu, K. Y. K. (2021) · Enterprise Information Systems · doi:10.1080/17517575.2021.1998641 · Cited by 8

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multiagent Scenarios

Young, A., Taves, J., Elmquist, A., Benatti, S., Tasora, A., Serban, R., & Negruţ, D. (2021) · Journal of Computational and Nonlinear Dynamics · doi:10.1115/1.4053321 · Cited by 8

Radar and Lidar Deep Fusion: Providing Doppler Contexts to Time-of-Flight Lidar

Jin, Y., Kuang, Y., Hoffmann, M., Schüßler, C., Deligiannis, A., Fuentes-Michel, J.-C., & Vossiek, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3313093 · Cited by 9

Camera Based AI Models Used with LiDAR Data for Improvement of Detected Object Parameters

Nowakowski, M., Kurylo, J., & Dang, P. H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71397-2_18 · Cited by 7

An Approach to Modeling and Developing Virtual Sensors Used in the Simulation of Autonomous Vehicles

Barabás, I., Iclodean, C. D., Beles, H., Antonya, C., Molea, A., & Scurt, F. B. (2025) · Sensors · doi:10.3390/s25113338 · Cited by 7

Simulation Techniques of the ADAS Perception Sensors: Review

Turcian, D., & Dolga, V. (2021) · Robotica & Management · doi:10.24193/rm.2021.2.7

Autonomous vehicle perception: The technology of today and tomorrow

Van Brummelen, J., O’Brien, M., Gruyer, D., & Najjaran, H. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.02.012 · Cited by 869

Enhancing Sensor Capabilities of Open-Source Simulation Tools to Support Autonomous Vehicles Safety Validation

Molina, C., Vismari, L. F., Fuji, T., Camargo, J. B., de Almeida, J. R., Inam, R., Fersman, E., Hata, A., & Marquezini, M. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99229-7_30 · Cited by 3

Modelling of aluminium plasma etch processes

Colson, P. M. F. (1991) · Microelectronic Engineering · doi:10.1016/0167-9317(91)90138-4

Simulation based perception testing for autonomous vehicles

Yang, X. (2022) · doi:10.32657/10356/154942 · Cited by 1