AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

A survey of transfer learning

Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260

Design and use paradigms for gazebo, an open-source multi-robot simulator

Koenig, N., & Howard, A. (2005) · 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) · doi:10.1109/iros.2004.1389727 · Cited by 3,638

Reinforcement learning in robotics: A survey

Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160

An introduction to inertial navigation

Woodman, O. J. (2021) · doi:10.48456/tr-696 · Cited by 797

RotorS—A Modular Gazebo MAV Simulator Framework

Furrer, F., Burri, M., Achtelik, M., & Siegwart, R. (2016) · Studies in Computational Intelligence · doi:10.1007/978-3-319-26054-9_23 · Cited by 592

PIXHAWK: A system for autonomous flight using onboard computer vision

Meier, L., Tanskanen, P., Fraundorfer, F., & Pollefeys, M. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980229 · Cited by 383

Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo

Meyer, J., Sendobry, A., Kohlbrecher, S., Klingauf, U., & von Stryk, O. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34327-8_36 · Cited by 342

Practical Meteorology : An Algebra-based Survey of Atmospheric Science

Stull, R. B. (2016) · cIRcle (University of British Columbia) · doi:10.14288/1.0300441 · Cited by 265

Introduction to Fluid Mechanics

(2011) · CRC Press eBooks · doi:10.1201/b12301-14 · Cited by 236

Classical Mechanics

Taylor, J. R. (2002) · CERN Document Server (European Organization for Nuclear Research) · Cited by 217

An Autonomous Intercept Drone with Image-based Visual Servo

Yang, K., & Quan, Q. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197539 · Cited by 27

MADRaS : Multi Agent Driving Simulator

Santara, A., Rudra, S., Buridi, S. A., Kaushik, M., Naik, A., Kaul, B., & Ravindran, B. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12531 · Cited by 27

AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

Samak, T. V., Samak, C. V., & Xie, M. (2021) · International Conference on Control, Robotics and Intelligent System (CCRIS) · doi:10.1145/3483845.3483846 · Cited by 27

Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter

Duisterhof, B. P., Krishnan, S., Cruz, J. J., Banbury, C., Fu, W., Faust, A., de Croon, G., & Reddi, V. J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561590 · Cited by 27

BMT: Behavior Driven Development-based Metamorphic Testing for Autonomous Driving Models

Deng, Y., Lou, G., Zheng, X., Zhang, T., Kim, M., Liu, H., Wang, C., & Chen, T. Y. (2021) · IEEE/ACM International Workshop on Metamorphic Testing (MET) · doi:10.1109/met52542.2021.00012 · Cited by 27

SemSegMap – 3D Segment-based Semantic Localization

Cramariuc, A., Tschopp, F., Alatur, N., Benz, S., Falck, T., Brühlmeier, M., Hahn, B. P., Nieto, J. I., & Siegwart, R. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636156 · Cited by 27

Involvement of Deep Learning for Vision Sensor-Based Autonomous Driving Control: A Review

Khanum, A., Lee, C.-Y., & Yang, C. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3280959 · Cited by 29

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 27

A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration

Huang, Z., Bai, L., Sun, M., & Cheng, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3461877 · Cited by 26

UA-MPC: Uncertainty-Aware Model Predictive Control for Motorized LiDAR Odometry

Li, J., Xu, X., Liu, J., Cao, K., Yuan, S., & Xie, L. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3544978 · Cited by 26

Autonomous and Cooperative Design of the Monitor Positions for a Team of UAVs to Maximize the Quantity and Quality of Detected Objects

Koutras, D. I., Kapoutsis, A. C., & Kosmatopoulos, E. B. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3004780 · Cited by 24

An Adversarial Approach to Private Flocking in Mobile Robot Teams

Zheng, H., Panerati, J., Beltrame, G., & Prorok, A. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.49744 · Cited by 26

Autoferry Gemini: a real-time simulation platform for electromagnetic radiation sensors on autonomous ships

Vasstein, K., Brekke, E., Mester, R., & Eide, E. S. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/929/1/012032 · Cited by 26

HammerDrive: A Task-Aware Driving Visual Attention Model

Amadori, P. V., Fischer, T., & Demiris, Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3055120 · Cited by 31

LiDAR Simulation for Performance Evaluation of UAS Detect and Avoid

Riordan, J. F., Manduhu, M., Black, J., Dow, A., Dooly, G., & Matalonga, S. (2021) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas51884.2021.9476817 · Cited by 28

SocialGym: A Framework for Benchmarking Social Robot Navigation

Holtz, J., & Biswas, J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982021 · Cited by 26

MIND MELD: Personalized Meta-Learning for Robot-Centric Imitation Learning

Schrum, M., Hedlund-Botti, E., Moorman, N. M., & Gombolay, M. (2022) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889616 · Cited by 26

Reinforcement Learning Based Topology Control for UAV Networks

Yoo, T., Lee, S., Yoo, K., & Kim, H. (2023) · Sensors · doi:10.3390/s23020921 · Cited by 26

Vision-based environment perception and autonomous obstacle avoidance for unmanned underwater vehicle

Yao, P., Sui, X., Liu, Y., & Zhao, Z. (2023) · Applied Ocean Research · doi:10.1016/j.apor.2023.103510 · Cited by 26

PMWorld: A Parallel Testing Platform for Autonomous Driving in Mines

Ai, Y., Liu, Y., Gao, Y. B., Zhao, C., Cheng, X., Han, J., Tian, B., Chen, L., & Wang, F. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3332739 · Cited by 26

Dealing with uncertainty: Balancing exploration and exploitation in deep recurrent reinforcement learning

Zangirolami, V., & Borrotti, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111663 · Cited by 26

Robots Saving Lives: A Literature Review About Search and Rescue (SAR) in Harsh Environments

Tong, K., Hu, Y., Đikić, B., Solmaz, S., Fraundorfer, F., & Watzenig, D. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588685 · Cited by 25

Visual route following for tiny autonomous robots

van Dijk, T., Wagter, C. D., & de Croon, G. (2024) · Science Robotics · doi:10.1126/scirobotics.adk0310 · Cited by 31

Optimized UAV view planning for high-quality 3D reconstruction of buildings using a modified sparrow search algorithm

Liang, Z., Yang, L., Liang, Z., Chan, J. C. F., Zhang, Z., Wang, M., & Cheng, J. C. P. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103344 · Cited by 25

Mobile Target Tracking Mechanisms Using Unmanned Aerial Vehicle: Investigations and Future Directions

Hentati, A. I., & Fourati, L. C. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2941452 · Cited by 25

Voxel-Based Representation Learning for Place Recognition Based on 3D Point Clouds

Siva, S., Nahman, Z., & Zhang, H. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340992 · Cited by 25

Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking

Glaser, N., Liu, Y., Tian, J., & Kira, Z. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636761 · Cited by 25

Simulated Photorealistic Deep Learning Framework and Workflows to Accelerate Computer Vision and Unmanned Aerial Vehicle Research

Alvey, B. J., Anderson, D. T., Buck, A. R., Deardorff, M., Scott, G. J., & Keller, J. M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00435 · Cited by 27

Model in the Loop Testing and Validation of Embedded Autonomous Driving Algorithms

Bruggner, D., Hegde, A., Acerbo, F. S., Gulati, D., & Son, T. D. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575530 · Cited by 25

Memory-enhanced deep reinforcement learning for UAV navigation in 3D environment

Fu, C., Xu, X., Zhang, Y., Lyu, Y., Xia, Y., Zhou, Z., & Wu, W. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07244-y · Cited by 25

Design and Implementation of Digital Twin-Assisted Simulation Method for Autonomous Vehicle in Car-Following Scenario

Xiong, H., Wang, Z., Wu, G., & Pan, Y. (2022) · Journal of Sensors · doi:10.1155/2022/4879490 · Cited by 27

Communication Efficient, Distributed Relative State Estimation in UAV Networks

Wang, S., Wang, Y., Bai, X., & Li, D. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242708 · Cited by 25

LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF

Zhu, Z., Chen, Y., Wu, Z., Hou, C., Shi, Y., Li, C., Li, P., Zhao, H., & Zhou, G. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161570 · Cited by 25

Human-Robot Interaction in a Shared Augmented Reality Workspace

Qiu, S., Liu, H., Zhang, Z., Zhu, Y., & Zhu, S. C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340781 · Cited by 28

Collision Avoidance Mechanism for Swarms of Drones

Marek, D., Biernacki, P., Szyguła, J., Domański, A., Paszkuta, M., Szczygieł, M., Król, M., & Wojciechowski, K. W. (2025) · Sensors · doi:10.3390/s25041141 · Cited by 31

Integrating tools for an effective testing of connected and automated vehicles technologies

Pariota, L., Coppola, A., Costanzo, L. D., Vico, A. D., Andolfi, A., D’Aniello, C., & Bifulco, G. N. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2019.0678 · Cited by 24

MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles

Hsiao, Y.-S., Wan, Z., Jia, T., Ghosal, R., Mahmoud, A., Raychowdhury, A., Brooks, D., Wei, G.-Y., & Reddi, V. J. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10137246 · Cited by 24

Learning to Simulate Realistic LiDARs

Guillard, B., Vemprala, S. H., Gupta, J. K., Mikšík, O., Vineet, V., Fua, P., & Kapoor, A. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981120 · Cited by 24

Rethinking Closed-Loop Training for Autonomous Driving

Zhang, W., Guo, R., Zeng, W., Xiong, Y., Dai, B., Hu, R., Ren, M., & Urtasun, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_16 · Cited by 25

Analyzing CARLA ’s performance for 2D object detection and monocular depth estimation based on deep learning approaches

Tabata, A. N., Zimmer, A., Coelho, L. D. S., & Mariani, V. C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120200 · Cited by 24

Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

Müller, M., Casser, V., Smith, N., Michels, D. L., & Ghanem, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_2 · Cited by 24

The Reasonable Effectiveness of Synthetic Visual Data

Gaidon, A., López, A. M., & Perronnin, F. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1108-0 · Cited by 25

Robotic Computing on FPGAs

Liu, S., Wan, Z., Bo, Y., & Wang, Y. (2021) · Synthesis lectures on computer architecture · doi:10.1007/978-3-031-01771-1 · Cited by 31

Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor EnvironmentsPreprint

Maciel-Pearson, B. G., Marchegiani, L., Akçay, S., Atapour–Abarghouei, A., Garforth, J., & Breckon, T. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05684 · Cited by 37

Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities

Amendola, J., Cenkeramaddi, L. R., & Jha, A. (2024) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2024.3444487 · Cited by 26

S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation

Chen, Y., Zhang, J., Xie, Z., Li, W., Zhang, F., Lu, J., & Zhang, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3543072 · Cited by 37

Learning Accurate, Comfortable and Human-like Driving

Hecker, S., Dai, D., & Gool, L. V. (2019) · Lirias · doi:10.48550/arxiv.1903.10995 · Cited by 28

A Framework for Self-Training Perceptual Agents in Simulated Photorealistic Environments

Mania, P., & Beetz, M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793474 · Cited by 23

Accurate Object Association and Pose Updating for Semantic SLAM

Chen, K., Liu, J., Chen, Q., Wang, Z., & Zhang, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3136918 · Cited by 23

Simulation Testbeds and Frameworks for UAV Performance Evaluation

Gill, J. S., Velashani, M. S., Wolf, J., Kenney, J., Manesh, M. R., & Kaabouch, N. (2021) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit51626.2021.9491882 · Cited by 23

Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning

Zurbrügg, R., Blum, H., Cadena, C., Siegwart, R., & Schmid, L. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3188901 · Cited by 23

ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

Ramakrishna, S., Luo, B., Kuhn, C. B., Karsai, G., & Dubey, A. K. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9921776 · Cited by 23

Recent advances in drone-view object detection

Leng, J., Mo, M., Zhou, Y., Ye, Y., Gao, C., & Gao, X. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220836 · Cited by 25

Pred-NBV: Prediction-Guided Next-Best-View Planning for 3D Object Reconstruction

Dhami, H., Sharma, V. D., & Tokekar, P. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341650 · Cited by 23

Deep Reinforcement Learning for Autonomous Search and Rescue

Cárcamo, J. G., Leidig, J. P., Trefftz, C. I., & Wolffe, G. (2018) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2018.8556642 · Cited by 23

VIVID

Lai, K.-T., Lin, C.-C., Kang, C.-Y., Liao, M.-E., & Chen⋆, M.-S. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3243653 · Cited by 23

Uncertainty-based modulation for lifelong learning

Brna, A., Brown, R. C., Connolly, P. M., Simons, S. B., Shimizu, R. E., & Aguilar, M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.011 · Cited by 23

A Study on the Challenges of Using Robotics Simulators for TestingPreprint

Afzal, A., Katz, D. S., Goues, C. L., & Timperley, C. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07368 · Cited by 31

Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration DataPreprint

He, L., Aouf, N., Whidborne, J. F., & Song, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02521 · Cited by 23

Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent VehiclesPreprint

Tian, Y., Carballo, A., Li, R., & Takeda, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13588 · Cited by 34

LiDAR4D: Dynamic Neural Fields for Novel Space-Time View LiDAR Synthesis

Zheng, Z., Lü, F., Xue, W., Chen, G., & Jiang, C. (2024) · 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/cvpr52733.2024.00492 · Cited by 22

GridNet: Image-Agnostic Conditional Anomaly Detection for Indoor Surveillance

Bozcan, İ., Sejersen, J. L. F., Pham, H. X., & Kayacan, E. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3057003 · Cited by 22

Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach

de Figueiredo, R. P., Sejersen, J. L. F., Hansen, J. G., Brandão, M., & Kayacan, E. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635986 · Cited by 22

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

Pyke, L. M., & Stark, C. R. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2021.744955 · Cited by 22

System configuration of Human-in-the-loop Simulation for Level 3 Autonomous Vehicle using IPG CarMaker

Hong, C. J., & Aparow, V. R. (2021) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais53735.2021.9628587 · Cited by 22

Systematic literature review of applications and usage potentials for the combination of unmanned aerial vehicles and mobile robot manipulators in production systems

Sinnemann, J., Boshoff, M., Dyrska, R., Leonow, S., Mönnigmann, M., & Kuhlenkötter, B. (2022) · Production Engineering · doi:10.1007/s11740-022-01109-y · Cited by 26

Residual Learning to Integrate Neural Network and Physics-Based Models for Improved Production Prediction in Unconventional Reservoirs

Cornelio, J., Razak, S. M., Cho, Y. H., Liu, H., Vaidya, R. N., & Jafarpour, B. (2022) · SPE Journal · doi:10.2118/210559-pa · Cited by 22

MarsSim: A high-fidelity physical and visual simulation for Mars rovers

Zhou, R., Feng, W., Ding, L., Yang, H., Gao, H., Liu, G., & Deng, Z. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3207705 · Cited by 22

Driver Profile Modeling Based on Driving Style, Personality Traits, and Mood States

Liao, X., Mehrotra, S., Ho, S., Gorospe, Y., Wu, X., & Mistu, T. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9921996 · Cited by 22

Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer

Sobczak, Ł., Filus, K., Domańska, J., & Domański, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-22938-y · Cited by 22

A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones

Zeng, L., Chen, H., Feng, D., Zhang, X., & Chen, X. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3297876 · Cited by 22

Co-simulation Digital Twin Framework for Testing Future Advanced Air Mobility Concepts: A Study with BlueSky and AirSim

Zhao, J., Conrad, C., Fremond, R., Mukherjee, A., Delezenne, Q., Su, Y., Xu, Y., & Tsourdos, A. (2023) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc58513.2023.10311124 · Cited by 22

Overcome the Fear Of Missing Out: Active sensing UAV scanning for precision agriculture

Krestenitis, M., Raptis, E. K., Kapoutsis, A. C., Ioannidis, K., Kosmatopoulos, E. B., & Vrochidis, S. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104581 · Cited by 22

System Design of an Open-Source Cloud-Based Framework for Internet of Drones Application

Mehrooz, G., Ebeid, E., & Schneider–Kamp, P. (2019) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2019.00087 · Cited by 22

Global optical flow-based estimation of velocity for multicopters using monocular vision in GPS-denied environments

Deng, H., Arif, U., Yang, K., Xi, Z.-Y., Quan, Q., & Cai, K.-Y. (2020) · Optik · doi:10.1016/j.ijleo.2020.164923 · Cited by 24

Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models

Munoz, S. A., Vernon, C. A., Hammond, J. E., Newell, V., Janson, J., Franke, K. W., & Hedengren, J. D. (2020) · Remote Sensing · doi:10.3390/rs12132169 · Cited by 22

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction

Wong, K., Zhang, Q., Liang, M., Yang, B., Liao, R., Sadat, A., & Urtasun, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_19 · Cited by 22

Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcement Learning for Search Missions

Sadhu, V., Sun, C., Karimian, A., Tron, R., & Pompili, D. (2020) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass50613.2020.00030 · Cited by 22

Blind spot detection for safe sim-to-real transfer

Ramakrishnan, R., Kamar, E., Dey, D., Horvitz, E., & Shah, J. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11436 · Cited by 21

Generating Reality-Analogous Datasets for Autonomous UAV Navigation using Digital Twin Areas

Lee, T., McKeever, S., & Courtney, J. (2022) · Irish Signals and Systems Conference · doi:10.1109/issc55427.2022.9826198 · Cited by 21

All for one, and one for all: UrbanSyn Dataset, the third Musketeer of synthetic driving scenes

Gómez, J. L., da Silva, M. A. F. M., Seoane, A., Borràs, A., Noriega, M., Ros, G., Iglesias-Guitián, J. A., & López, A. M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130038 · Cited by 43

Multi-Modal Intelligent Channel Modeling: A New Modeling Paradigm via Synesthesia of Machines

Bai, L., Huang, Z., Sun, M., Cheng, X., & Cui, L. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3558046 · Cited by 21

Collaborative Perception Datasets for Autonomous Driving: A Review

Wang, N. Y., Shang, D., Gong, Y., Hu, X., Song, Z., Yang, L., Huang, Y., Wang, X., & Lu, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3582040 · Cited by 30

Multimodal 3D Object Detection from Simulated Pretraining

Brekke, Å., Vatsendvik, F., & Lindseth, F. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-35664-4_10 · Cited by 25

Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation

Akhauri, S., Zheng, L., & Lin, M. C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341538 · Cited by 21

DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds

Kwak, J., & Sung, Y. (2021) · Remote Sensing · doi:10.3390/rs13081565 · Cited by 21

Survey on Autonomous Vehicle Simulation Platforms

Yang, G., Xue, Y., Meng, L., Wang, P., Shi, Y., Yang, Q.-H., & Dong, Q. (2021) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa52907.2021.00100 · Cited by 21

Video super-resolution for single-photon LIDAR

Martín, G. M., Scholes, S., Ruget, A., Henderson, R., Leach, J., & Gyöngy, I. (2023) · Optics Express · doi:10.1364/oe.478308 · Cited by 21

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Li, H., Cai, Y., Hong, J., Xu, P., Cheng, H., Zhu, X., Hu, B., Hao, Z., & Fan, Z. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3236565 · Cited by 23

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Yang, Z., Manivasagam, S., Chen, Y., Wang, J., Hu, R., & Urtasun, R. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160535 · Cited by 21

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Wen, L., Fu, D., Mao, S., Cai, P., Dou, M., Li, Y., & Qiao, Y. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422219 · Cited by 25

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Koubâa, A. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-20190-6 · Cited by 24

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Lélis, C. A. S., Roncal, J. J., Silveira, L., de Aquino, R. D. G., Marcondes, C. A. C., Marques, J., Loubach, D. S., Verri, F. A. N., Curtis, V., & Souza, D. G. D. (2024) · IEEE Access · doi:10.1109/access.2024.3462436 · Cited by 20

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Walker, S. M., Shan, J., & Allison, R. S. (2016) · AIAA Modeling and Simulation Technologies Conference · doi:10.2514/6.2016-1667