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

A survey of transfer learning

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

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Reinforcement learning in robotics: A survey

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An introduction to inertial navigation

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RotorS—A Modular Gazebo MAV Simulator Framework

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PIXHAWK: A system for autonomous flight using onboard computer vision

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Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo

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Practical Meteorology : An Algebra-based Survey of Atmospheric Science

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Introduction to Fluid Mechanics

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Classical Mechanics

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Brain inspired path planning algorithms for drones

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Optimizing Fiducial Marker Placement for Improved Visual Localization

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Monocular Visual-Inertial Depth Estimation

Wofk, D., Ranftl, R., Müller, M., & Koltun, V. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161013 · Cited by 18

Efficient virtual-to-real dataset synthesis for amodal instance segmentation of occlusion-aware rockfill material gradation detection

Hu, Y., Wang, J., Wang, X., Yu, J., & Zhang, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122046 · Cited by 17

NeuroNCAP: Photorealistic Closed-Loop Safety Testing for Autonomous Driving

Ljungbergh, W., Tonderski, A., Johnander, J., Caesar, H., Åström, K., Felsberg, M., & Petersson, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73404-5_10 · Cited by 26

Autonomous Driving Small-Scale Cars: A Survey of Recent Development

Li, D., Auerbach, P., & Okhrin, O. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3572394 · Cited by 28

Video-guided Camera Control for Target Tracking and Following

Gemerek, J., Ferrari, S., Wang, B. H., & Campbell, M. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.01.062 · Cited by 16

CrazyS: A Software-in-the-Loop Simulation Platform for the Crazyflie 2.0 Nano-Quadcopter

Silano, G., & Iannelli, L. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-20190-6_4 · Cited by 17

Autonomous-Driving Vehicle Learning Environments using Unity Real-time Engine and End-to-End CNN Approach

Hossain, S., & Lee, D. J. (2019) · The Journal of Korea Robotics Society · doi:10.7746/jkros.2019.14.2.122 · Cited by 16

A synthetic dataset for Visual SLAM evaluation

Wang, S., Yue, J., Dong, Y., He, S., Wang, H., & Ning, S. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.103336 · Cited by 16

New Simulation Tools for Training and Testing Automated Vehicles

Ma, J., Schwarz, C., Wang, Z., Elli, M. S., Ros, G., & Feng, Y. (2020) · Lecture notes in mobility · doi:10.1007/978-3-030-52840-9_11 · Cited by 16

Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy

Zhao, Y., Smith, J. S., Karumanchi, S. H., & Vela, P. A. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197003 · Cited by 15

Hardware-in-the-loop simulation with dynamic partial FPGA reconfiguration applied to computer vision in ROS-based UAV

Moréac, E., Abdali, E. M., Berry, F., Heller, D., & Diguet, J.-P. (2020) · International Workshop on Rapid System Prototyping (RSP) · doi:10.1109/rsp51120.2020.9244863 · Cited by 16

CCAST

Clark, S. M., Usbeck, K., Diller, D., & Schantz, R. E. (2021) · GetMobile Mobile Computing and Communications · doi:10.1145/3457356.3457362 · Cited by 16

Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving

Ding, Z., & Xiang, J. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12040222 · Cited by 16

CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator

Jang, J., Lee, H., & Kim, J.-C. (2021) · Applied Sciences · doi:10.3390/app12010281 · Cited by 18

SynchroSim: An Integrated Co-simulation Middleware for Heterogeneous Multi-robot System

Dey, E., Hossain, J., Roy, N., & Busart, C. (2022) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss54816.2022.00061 · Cited by 17

Optical flow for autonomous driving: Applications, challenges and improvements

Shen, S., Kerofsky, L. J., & Yogamani, S. K. (2023) · Electronic Imaging · doi:10.2352/ei.2023.35.16.avm-128 · Cited by 16

Human Attribute Recognition— A Comprehensive Survey

Yaghoubi, E., Khezeli, F., Borza, D. L., Kumar, S. A., Neves, J. C., & Proença, H. P. M. C. (2020) · Applied Sciences · doi:10.3390/app10165608 · Cited by 19

Flight Controller Synthesis Via Deep Reinforcement LearningPreprint

Koch, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06493 · Cited by 18

3DB: A Framework for Debugging Computer Vision ModelsPreprint

Leclerc, G., Salman, H., Ilyas, A., Vemprala, S. H., Engstrom, L., Vineet, V., Xiao, K., Zhang, P., Santurkar, S., Yang, G., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03805 · Cited by 16

AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis

Vuong, K., Ghosh, A., Ramanan, D., Narasimhan, S. G., & Tulsiani, S. (2025) · 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/cvpr52734.2025.02019 · Cited by 15

LidarDM: Generative LiDAR Simulation in a Generated World

Zyrianov, V., Che, H., Liu, Z., & Wang, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128001 · Cited by 56

Delving Into the Secrets of BEV 3D Object Detection in Autonomous Driving: A Comprehensive Survey

Li, H., Zhao, Y., Zhong, J., Wang, B., Sun, C., & Sun, F. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3624830 · Cited by 22

3D Human Reconstruction in the Wild with Collaborative Aerial Cameras

Ho, C., Jong, A., Freeman, H., Rao, R., Bonatti, R., & Scherer, S. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636745 · Cited by 15

A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems

Pianini, D., Pettinari, F., Casadei, R., & Esterle, L. (2022) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3547145 · Cited by 15

Design Guidelines of the Internet of Drones Location Privacy Protocols

Svaigen, A. R., Boukerche, A., Ruiz, L. B., & Loureiro, A. A. F. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.008.2100131 · Cited by 15

Variable Baseline and Flexible Configuration Stereo Vision Using Two Aerial Robots

Sumetheeprasit, B., Martinez, R. R., Paul, H., Ladig, R., & Shimonomura, K. (2023) · Sensors · doi:10.3390/s23031134 · Cited by 15

Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

Yu, X., Queralta, J. P., & Westerlund, T. (2022) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec57183.2022.10062657 · Cited by 15

A Digital Twin Mixed-reality System for Testing Future Advanced Air Mobility Concepts: A Prototype

Zhao, J., Conrad, C., Delezenne, Q., Xu, Y., & Tsourdos, A. (2023) · Integrated Communications, Navigation and Surveillance Conference (ICNS) · doi:10.1109/icns58246.2023.10124310 · Cited by 16

HMAAC: Hierarchical Multi-Agent Actor-Critic for Aerial Search with Explicit Coordination Modeling

Sun, C., Huang, S., & Pompili, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161019 · Cited by 15

Vision-Based Multiobject Tracking Through UAV Swarm

Shen, H., Lin, D., Yang, X., & He, S. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3305675 · Cited by 15

From Words to Flight: Integrating OpenAI ChatGPT with PX4/Gazebo for Natural Language-Based Drone Control

(2023) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2023.06.031 · Cited by 19

UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment

Li, X., Fang, J., Du, K., Mei, K., & Xue, J. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240962 · Cited by 15

Range Specification Bug Detection in Flight Control System Through Fuzzing

Han, R., Ma, S., Li, J., Nepal‬, ‪., Lo, D., Ma, Z., & Ma, J. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3354739 · Cited by 15

Robotics Meets Fluid Dynamics: A Characterization of the Induced Airflow Below a Quadrotor as a Turbulent Jet

Bauersfeld, L., Muller, K., Ziegler, D., Coletti, F., & Scaramuzza, D. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3518835 · Cited by 15

MineSim: A scenario-based simulation test system and benchmark for autonomous trucks in open-pit mines

Chen, Z., Yu, G., Chen, P., Cao, G., Li, Z., Zhang, Y., Ni, H., Zhou, B., Sun, J., & Ban, H. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.107938 · Cited by 16

Navigating Assistance System for Quadcopter with Deep Reinforcement Learning

Wu, T.-C., Tseng, S.-Y., Lai, C., Ho, C.-Y., & Lai, Y.-H. (2018) · International Cognitive Cities Conference (IC3) · doi:10.1109/ic3.2018.00013 · Cited by 15

FANTASIA: a framework for advanced natural tools and applications in social, interactive approaches

Origlia, A., Cutugno, F., Rodà, A., Cosi, P., & Zmarich, C. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7362-5 · Cited by 15

Reducing Drone Incidents by Incorporating Human Factors in the Drone and Drone Pilot Accreditation Process

Doroftei, D., Cubber, G. D., & Smet, H. D. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51758-8_10 · 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

Simulation-based Digital Twin for 5G Connected Automated and Autonomous Vehicles

Barbi, M., Ruiz, A. A., Mrozowski, A., Inca, S., García‐Roger, D., & Monserrat, J. F. (2022) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit54941.2022.9815732 · Cited by 15

Validation of Physics-Based Image Systems Simulation With 3-D Scenes

Lyu, Z., Goossens, T., Wandell, B. A., & Farrell, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3199699 · Cited by 15

Distributed deep reinforcement learning on the cloud for autonomous driving

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Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and ChallengesPreprint

Zhou, Z., Liu, G., & Tang, Y. G. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.10091 · Cited by 15

What Matters in Learning a Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study

Chen, J., Yu, C., Xie, Y., Gao, F., Chen, Y., Yu, S., Tang, W., Ji, S., Mu, M., Wu, Y., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3575011 · Cited by 14

Use of multimodal physiological signals to explore pilots’ cognitive behaviour during flight strike task performance

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Effects of Augmented-Reality-Based Assisting Interfaces on Drivers’ Object-wise Situational Awareness in Highly Autonomous Vehicles

Gao, X., Wu, X., Ho, S., Misu, T., & Akash, K. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827008 · Cited by 15

Continuous Safety Control of Mobile Robots in Cluttered Environments

Wu, S., Liu, T., Niu, Q., & Jiang, Z. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3187492 · Cited by 14

RPNet: Rotational pooling net for efficient Micro Aerial Vehicle trail navigation

Agyemang, I. O., Zhang, X., Acheampong, D., Adjei-Mensah, I., Gyamfi, E. O., Arhin, J. R., Ayivi, W., & Chikwendu, I. A. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105468 · Cited by 14

Fisher Information Based Active Planning for Aerial Photogrammetry

Lim, J., Lawrance, N., Achermann, F., Stastny, T., Bähnemann, R., & Siegwart, R. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161136 · Cited by 14

Orientation-Aware Hierarchical, Adaptive-Resolution A* Algorithm for UAV Trajectory Planning

Funk, N., Tarrio, J., Papatheodorou, S., Alcantarilla, P. F., & Leutenegger, S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3308490 · Cited by 14

SPIRAL: An efficient algorithm for the integration of the equation of rotational motion

del Valle, C. A., Angelidakis, V., Roy, S., Muñoz, J. D., & Pöschel, T. (2024) · Computer Physics Communications · doi:10.1016/j.cpc.2023.109077 · Cited by 14

Influence of Realistic Perception and Surroundings on Qualitative Results in Automated and Connected Vehicle Simulation

Stepanyants, V. G., & Romanov, A. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3380369 · Cited by 14

Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

Lee, C.-Y., Khanum, A., & Sung, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19601-1 · Cited by 14

Network simulation tools for unmanned aerial vehicle communications: A survey

Jiang, W., Han, H., He, M., & Gu, W. (2024) · International Journal of Communication Systems · doi:10.1002/dac.5878 · Cited by 14

UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception

Maxey, C., Choi, J., Lee, H., Manocha, D., & Kwon, H. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611523 · Cited by 14

APACE: Agile and Perception-aware Trajectory Generation for Quadrotor Flights

Chen, X., Zhang, Y., Zhou, B., & Shen, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610150 · Cited by 14

Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms

Vemprala, S. H., & Saripalli, S. (2018) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2018.8453412 · Cited by 14

Planning Beyond The Sensing Horizon Using a Learned Context

Everett, M., Miller, J., & How, J. P. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967550 · Cited by 14

Proactive Intention Recognition for Joint Human-Robot Search and Rescue Missions Through Monte-Carlo Planning in POMDP Environments

Ognibene, D., Mirante, L., & Marchegiani, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35888-4_31 · Cited by 14

Simulating use cases for the UAH Autonomous Electric Car

Gómez-Huélamo, C., Bergasa, L. M., Barea, R., López-Guillén, E., Arango, F., & Sanchez, P. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917017 · Cited by 14

An Autonomous UAV Navigation System for Unknown Flight Environment

Huang, H., Gu, J., Wang, Q.-H., & Zhuang, Y. (2019) · International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) · doi:10.1109/msn48538.2019.00025 · 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

Obstacle Avoidance Using a Monocular Camera

Hatch, K., Mern, J., & Kochenderfer, M. J. (2021) · AIAA SciTech Forum · doi:10.2514/6.2021-0269 · Cited by 15

World-in-the-Loop Simulation for Autonomous Systems Validation

Hildebrandt, C., & Elbaum, S. G. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561240 · Cited by 14

Autonomous Driving Based on Modified SAC Algorithm through Imitation Learning Pretraining

Gao, M., & Chang, D. E. (2021) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas52745.2021.9649939 · Cited by 14

Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs

Savkin, A., Ellouze, R., Navab, N., & Tombari, F. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636318 · Cited by 15

SupSLAM: A Robust Visual Inertial SLAM System Using SuperPoint for Unmanned Aerial Vehicles

Quach, C. H., Phung, M. D., Le, H. V., & Perry, S. W. (2021) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics54270.2021.9701527 · Cited by 14

Vision-Based 2D Navigation of Unmanned Aerial Vehicles in Riverine Environments with Imitation Learning

Wei, P., Liang, R., Michelmore, A., & Kong, Z. (2022) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-022-01593-5 · Cited by 14

Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity

Buckman, N., Hansen, A., Karaman, S., & Rus, D. L. (2022) · Sensors · doi:10.3390/s22186793 · Cited by 14

Bio-mimetic high-speed target localization with fused frame and event vision for edge application

Lele, A. S., Fang, Y., Anwar, A., & Raychowdhury, A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1010302 · Cited by 14

SHREC 2023: Point cloud change detection for city scenes

Gao, Y., Yuan, H., Ku, T., Veltkamp, R. C., Ζαμανάκος, Γ., Tsochatzidis, L., Amanatiadis, A. A., Pratikakis, I., Panou, A., Romanelis, I., et al. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.06.025 · Cited by 14

HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO

Cioffi, G., Bauersfeld, L., & Scaramuzza, D. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.071 · Cited by 14

Multimodal Deep Learning for Advanced Driving Systems

Aranjuelo, N., Unzueta, L., Arganda‐Carreras, I., & Otaegui, O. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94544-6_10 · Cited by 14

An Approach for Semantic Segmentation of Tree-like Vegetation

Digumarti, S. T., Schmid, L., Rizzi, G. M., Nieto, J. I., Siegwart, R., Beardsley, P. A., & Cadena, C. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793576 · Cited by 14

Automatic Drone Navigation in Realistic 3D Landscapes using Deep Reinforcement Learning

Shin, S., Kang, Y.-W., & Kim, Y.-G. (2019) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2019.8820322 · Cited by 14

Multiple Event-Based Simulation Scenario Generation Approach for Autonomous Vehicle Smart Sensors and Devices

Park, J., Wen, M., Sung, Y., & Cho, K. (2019) · Sensors · doi:10.3390/s19204456 · Cited by 14

Identification of performance limitations of sensing technologies for automated driving

Martin, H., Winkler, B., Grubmüller, S., & Watzenig, D. (2019) · International Conference on Connected Vehicles and Expo · doi:10.1109/iccve45908.2019.8965181 · Cited by 14

Batteries, camera, action! Learning a semantic control space for expressive robot cinematography

Bonatti, R., Bucker, A., Scherer, S., Mukadam, M., & Hodgins, J. K. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560745 · Cited by 14

Learning Pothole Detection in Virtual Environment

Tsai, J.-C., Lai, K.-T., Dai, T.-C., Su, J.-J., Siao, C.-Y., & Hsu, Y.-C. (2020) · International Automatic Control Conference · doi:10.1109/cacs50047.2020.9289740 · Cited by 14

Image Synthesis Pipeline for CNN-Based Sensing Systems

Frolov, V. A., Faizov, B., Shakhuro, V., Санжаров, В., Konushin, A., Galaktionov, V. A., & Voloboy, A. G. (2022) · Sensors · doi:10.3390/s22062080 · Cited by 14

Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving

Guo, J., Kurup, U., & Shah, M. (2018) · arXiv · doi:10.48550/arxiv.1811.11277 · Cited by 14

Using Soft Actor-Critic for Low-Level UAV ControlPreprint

Barros, G. M., & Colombini, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02293 · Cited by 14

A Survey of Task Planning with Large Language Models

Zhai, W., Liao, J., Chen, Z., Su, B., & Zhao, X. (2025) · Intelligent Computing · doi:10.34133/icomputing.0124 · Cited by 21

A review on monocular tracking and mapping: from model-based to data-driven methods

Gadipudi, N., Elamvazuthi, I., Izhar, L. I., Tiwari, L., Hebbalaguppe, R., Lu, C., & Doss, A. S. A. (2022) · The Visual Computer · doi:10.1007/s00371-022-02702-z · Cited by 14

A simulator of underwater glider missions for path planning

Merci, A., Anthierens, C., Thirion‐Moreau, N., & Page, Y. L. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113514 · Cited by 13

Analysing the Factors Affecting the Long-term Investment Intention of Investors

Ferreira-Schenk, S., & Dickason–Koekemoer, Z. (2023) · International Journal of Economics and Financial Issues · doi:10.32479/ijefi.13640 · Cited by 13

Learning When to Use Adaptive Adversarial Image Perturbations Against Autonomous Vehicles

Yoon, H.-J., Jafarnejadsani, H., & Voulgaris, P. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3280813 · Cited by 13

Value of process understanding in the era of machine learning: A case for recession flow prediction

Istalkar, P., Kadu, A., & Biswal, B. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130350 · Cited by 14

Bag of Views: An Appearance-Based Approach to Next-Best-View Planning for 3D Reconstruction

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