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

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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

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

Introduction to Fluid Mechanics

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Classical Mechanics

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

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

Development of an Underwater Simulator using Unity3D and Robot Operating System

Chaudhary, A. S., Mishra, R., Kalyan, B., & Chitre, M. (2021) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans44145.2021.9706012 · Cited by 20

Photometric Visual-Inertial Navigation With Uncertainty-Aware Ensembles

Jung, J. H., Choe, Y., & Park, C. G. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3139964 · Cited by 20

Panorama-to-model registration through integration of image retrieval and semantic reprojection

Wei, Y., & Akinci, B. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104356 · Cited by 20

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

Software Aging Effects on Kubernetes in Container Orchestration Systems for Digital Twin Cloud Infrastructures of Urban Air Mobility

Costa, J., Junior, R. D. S. M., de Araujo, J. C. T., Li, J., Choi, E., Nguyễn, T. A., Lee, J.-W., & Min, D. (2023) · Drones · doi:10.3390/drones7010035 · Cited by 20

An agent-based modeling framework for the multi-UAV rendezvous recharging problem

Chour, K., Reddinger, J.-P., Dotterweich, J. M., Childers, M., Humann, J. D., Rathinam, S., & Darbha, S. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104442 · Cited by 20

Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network

Gan, L., Lee, C., & Chung, S. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160872 · Cited by 23

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Xu, H., Shao, Y., Chen, J. M., Wang, C. R., & Berres, A. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231205884 · 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

Quantitative Assessment of Drone Pilot Performance

Doroftei, D., Cubber, G. D., Bue, S. L., & Smet, H. D. (2024) · Drones · doi:10.3390/drones8090482 · Cited by 20

Image-based deep learning for smart digital twins: a review

Islam, M. R., Subramaniam, M., & Huang, P.-C. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11002-y · Cited by 25

Towards Modeling of Perception Errors in Autonomous Vehicles

Mitra, P., Choudhury, A., Aparow, V. R., Giridharan, K., & Dauwels, J. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8570015 · Cited by 20

Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving

Wang, T., & Chang, D. E. (2019) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas47443.2019.8971737 · Cited by 20

Learning to Assist Drone Landings

Backman, K., Kulić, D., & Chung, H. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062572 · Cited by 20

Train here, drive there: ROS based end-to-end autonomous-driving pipeline validation in CARLA simulator using the NHTSA typology

Gómez-Huélamo, C., Egido, J. D., Bergasa, L. M., Barea, R., López-Guillén, E., Arango, F., Araluce, J., & López, J. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11681-7 · Cited by 20

Deep learning for vision-based navigation in autonomous drone racing

Pham, H. X., Uğurlu, H. İ., Sejersen, J. L. F., Bardakci, D., & Kayacan, E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385787-1.00020-8 · Cited by 20

ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions

Sural, S., Sahu, N., & Rajkumar, R. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588584 · Cited by 19

U2UData: A Large-scale Cooperative Perception Dataset for Swarm UAVs Autonomous Flight

Feng, T., Wang, X., Han, F., Zhang, L., & Zhu, W. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681151 · Cited by 26

Urban Computing in the Era of Large Language Models

Li, Z., Xia, L., Ren, X., Tang, J., Chen, T., Xu, Y., & Huang, C. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3768163 · Cited by 33

Multi-Objective Autonomous Braking System using Naturalistic Dataset

Vasquez, R., & Farooq, B. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8916912 · Cited by 19

Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection

DeBortoli, R., Li, F., Kapoor, A., & Hollinger, G. A. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3093869 · Cited by 19

A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM

Fu, J., Huang, Q., Doherty, K., Wang, Y., & Leonard, J. J. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635956 · Cited by 19

Learning Interactive Driving Policies via Data-driven Simulation

Wang, T.-H. J., Amini, A., Schwarting, W., Gilitschenski, I., Karaman, S., & Rus, D. L. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812407 · Cited by 22

UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images

He, B., Li, X., Huang, B., Gu, E., Guo, W., & Wu, L. (2021) · Remote Sensing · doi:10.3390/rs13244999 · Cited by 19

Towards an Autonomous, Visual Inspection-aware 3D Exploration and Mapping System for Water Ballast Tanks of Marine Vessels

Brogaard, R. Y., Andersen, R. E., Kovač, L., Zajączkowski, M., & Boukas, E. (2021) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist50367.2021.9651476 · Cited by 19

A Virtual Method for Optimizing Deployment of Roadside Monitoring Lidars at As-Built Intersections

Ma, Y., Zheng, Y., Wang, S., Wong, Y. D., & Easa, S. M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3286384 · Cited by 19

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

Autonomous driving controllers with neuromorphic spiking neural networks

Halaly, R., & Tsur, E. E. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1234962 · Cited by 19

GINA-3D: Learning to Generate Implicit Neural Assets in the Wild

Shen, B., Yan, X., Qi, C. R., Najibi, M., Deng, B., Guibas, L. J., Zhou, Y., & Anguelov, D. (2023) · 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/cvpr52729.2023.00476 · Cited by 23

Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator

Zanatta, L., Mauro, A. D., Barchi, F., Bartolini, A., Benini, L., & Acquaviva, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126885 · Cited by 21

MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection

Álvarez-Tuñón, O., Kanner, H., Marnet, L. R., Pham, H. X., Sejersen, J. L. F., Brodskiy, Y., & Kayacan, E. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341436 · Cited by 23

Autonomous view planning methods for 3D scanning

Lee, I. D., Seo, J. H., & Yoo, B. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105291 · Cited by 19

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

Yu, Z., Wang, H., Yang, J., Wang, H., Cao, J., Ji, Z., & Sun, M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00375 · Cited by 41

DDFlow

Noor, J., Tseng, H.-Y., García, L. A. R., & Srivastava, M. B. (2019) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3302505.3310079 · Cited by 19

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Kolos, M., Marin, A., Artemov, A., & Burnaev, E. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22808-8_37 · Cited by 18

Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematography

Bucker, A., Bonatti, R., & Scherer, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561086 · Cited by 19

Deliberative acting, planning and learning with hierarchical operational models

Patra, S., Mason, J., Ghallab, M., Nau, D. S., & Traverso, P. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103523 · Cited by 20

Development and Experimental Validation of an Intelligent Camera Model for Automated Driving

Genser, S., Muckenhuber, S., Solmaz, S., & Reckenzaun, J. (2021) · Sensors · doi:10.3390/s21227583 · Cited by 19

Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerPreprint

Duisterhof, B. P., Krishnan, S., Cruz, J. J., Banbury, C., Fu, W., Faust, A., de Croon, G., & Reddi, V. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11236 · Cited by 19

From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

Zipfl, M., Fleck, T., Zofka, M. R., & Zöllner, J. M. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294539 · Cited by 18

Robust Tightly-Coupled Visual-Inertial Odometry with Pre-built Maps in High Latency Situations

Bao, H., Xie, W., Qian, Q., Chen, D., Zhai, S., Wang, N., & Zhang, G. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3150495 · Cited by 23

Display of 3D Illuminations using Flying Light Specks

Ghandeharizadeh, S. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548250 · Cited by 18

Countering a Drone in a 3D Space: Analyzing Deep Reinforcement Learning Methods

Çetin, E., Barrado, C., & Pastor, E. (2022) · Sensors · doi:10.3390/s22228863 · Cited by 18

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

Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing

Manivasagam, S., Bârsan, I. A., Wang, J., Yang, Z., & Urtasun, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00760 · Cited by 18

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

Ywet, N. L., Maw, A. A., Nguyễn, T. A., & Lee, J.-W. (2024) · Aerospace · doi:10.3390/aerospace11030179 · Cited by 21

SBC-SLAM: Semantic Bioinspired Collaborative SLAM for Large-Scale Environment Perception of Heterogeneous Systems

Liu, D., Wu, J., Du, Y., Zhang, R., & Cong, M. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3385825 · Cited by 20

CAVIAR: Co-Simulation of 6G Communications, 3-D Scenarios, and AI for Digital Twins

Borges, J., Bastos, F., Correa, I., Batista, P., & Junior, A. B. D. R. K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3418675 · Cited by 18

Hierarchical Memory System With STT-MRAM and SRAM to Support Transfer and Real-Time Reinforcement Learning in Autonomous Drones

Yoon, I., Anwar, M. A., Joshi, R., Rakshit, T., & Raychowdhury, A. (2019) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2019.2932285 · Cited by 19

Development of a Virtual Simulation Environment and a Digital Twin of an Autonomous Driving Truck for a Distribution Center

Barosan, I., Basmenj, A. A., Chouhan, S. G. R., & Manrique, D. (2020) · ECSA Companion · doi:10.1007/978-3-030-59155-7_39 · Cited by 21

U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

Wang, C., Li, C., Liu, J., Luo, B., Su, X., Wang, Y., & Gao, Y. J. (2020) · Remote Sensing · doi:10.3390/rs13010060 · Cited by 18

Vision-Based Uncertainty-Aware Lane Keeping Strategy Using Deep Reinforcement Learning

Kim, M., Seo, J., Lee, M., & Choi, J. (2021) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4050396 · Cited by 17

An Approach to Deploy Interactive Robotic Simulators on the Web for HRI Experiments: Results in Social Robot Navigation

Tsoi, N., Hussein, M., Fugikawa, O., Zhao, J., & Vázquez, M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636319 · Cited by 18

An Intelligent Self-Driving Truck System for Highway Transportation

Wang, D., Gao, L., Lan, Z., Li, W., Ren, J., Zhang, J., Zhang, P., Zhou, P., Wang, S., Pan, J., et al. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.843026 · Cited by 18

A Simulation-Based Development and Verification Architecture for Micro UAV Teams and Swarms

Akçakoca, M., Atici, B. M., Gever, B., Oğuz, S., Demi̇rezen, M. U., Demir, M., Saldiran, E., Yüksek, B., Koyuncu, E., Yeniçeri, R., et al. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-1979 · Cited by 18

Advancing Non-Contact Vital Sign Measurement using Synthetic AvatarsPreprint

McDuff, D., Hernandez, J., Wood, E., Liu, X., & Baltrušaitis, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12949 · Cited by 18

SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)

Cheng, X., Huang, Z., Yu, Y. M., Bai, L., Sun, M., Han, Z., Zhang, R., & Li, S. (2025) · Scientific Data · doi:10.1038/s41597-025-05065-x · Cited by 18

Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution

Ramakrishnan, R., Kamar, E. S., Nushi, B., Dey, D., Shah, J., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016137 · Cited by 17

Virtual Scenario Simulation and Modeling Framework in Autonomous Driving Simulators

Wen, M., Park, J., Sung, Y., Park, Y. W., & Cho, K. (2021) · Electronics · doi:10.3390/electronics10060694 · Cited by 17

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

Jamieson, S., Fathian, K., Khosoussi, K., How, J. P., & Girdhar, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561934 · Cited by 17

IoT Sensing for Reality-Enhanced Serious Games, a Fuel-Efficient Drive Use Case

Massoud, R., Berta, R., Poslad, S., Gloria, A. D., & Bellotti, F. (2021) · Sensors · doi:10.3390/s21103559 · Cited by 17

Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways

Sejersen, J. L. F., de Figueiredo, R. P., & Kayacan, E. (2021) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551637 · Cited by 17

A New Approach to Complex Dynamic Geofencing for Unmanned Aerial Vehicles

Vagal, V., Markantonakis, K., & Shepherd, C. (2021) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc52595.2021.9594499 · Cited by 17

Autonomous Drone Delivery to Your Door and Yard

Kannan, S. S., & Min, B. (2022) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas54217.2022.9836219 · Cited by 17

A Privacy-Preserving Solution for Intelligent Transportation Systems: Private Driver DNA

Costantino, G., Vincenzi, M. D., Martinelli, F., & Matteucci, I. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3217358 · Cited by 17

Dyna-PPO reinforcement learning with Gaussian process for the continuous action decision-making in autonomous driving

Wu, G., Fang, W., Wang, J., Ge, P., Cao, J., Yang, P., & Gou, P. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04354-x · Cited by 17

A domain-agnostic approach for characterization of lifelong learning systems

Baker, M. M., New, A., Aguilar, M., Al-Halah, Z., Arnold, S. M. R., Ben-Iwhiwhu, E., Brna, A., Brooks, E., Brown, R. C., Daniels, Z., et al. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.007 · Cited by 22

Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective

Hinniger, C., & Rüter, J. (2023) · Aerospace · doi:10.3390/aerospace10070604 · Cited by 17

AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors

Yu, H., de Croon, G., & Wagter, C. D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161097 · Cited by 21

Reinforcement learning for shared autonomy drone landings

Backman, K., Kulić, D., & Chung, H. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10143-3 · Cited by 17

Robust Cross-Drone Multi-Target Association Using 3D Spatial Consistency

Pan, T., Dong, H., Deng, B., Gui, J., & Zhao, B. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3341009 · Cited by 17

A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring

Xie, D., Hu, R., Wang, C., Zhu, C., Xu, H., & Li, Q. (2023) · Remote Sensing · doi:10.3390/rs15245758 · Cited by 17

CineMPC: A Fully Autonomous Drone Cinematography System Incorporating Zoom, Focus, Pose, and Scene Composition

Pueyo, P., Dendarieta, J., Montijano, E., Murillo, A. C., & Schwager, M. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3353550 · Cited by 17

FastSim: A Modular and Plug-and-Play Simulator for Aerial Robots

Cui, C., Zhou, X., Wang, M., Gao, F., & Xu, C. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3391059 · Cited by 20

Multi-Modal Sensing Data-Based Real-Time Path Loss Prediction for 6G UAV-to-Ground Communications

Sun, M., Bai, L., Huang, Z., & Cheng, X. (2024) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2024.3419245 · Cited by 17

AeroHaptix: A Wearable Vibrotactile Feedback System for Enhancing Collision Avoidance in UAV Teleoperation

Huang, B., Wang, Z., Cheng, Q., Ren, S., Cai, H., Valdivia, A. A., Mahadevan, K., & Wigdor, D. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3548866 · Cited by 17

An hybrid simulation tool for autonomous cars in very high traffic scenarios

Garzón, M., & Spalanzani, A. (2018) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2018.8581290 · Cited by 17

A Comprehensive Simulation Platform for Testing Autonomous Vehicles in 3D Virtual Environment

Aparow, V. R., Apratim, C., Giridharan, K., Webster, T., Dauwels, J., & de Boer, N. (2019) · IEEE International Conference on Mechatronics System and Robots (ICMSR) · doi:10.1109/icmsr.2019.8835477 · Cited by 17

UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS

Osborne, M., Lantair, J., Shafiq, Z., Zhao, X., Robu, V., Flynn, D., & Perry, J. (2019) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs48664.2019.8987692 · Cited by 17

Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles

Sharma, V. D., Toubeh, M., Zhou, L., & Tokekar, P. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341075 · Cited by 17

Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images

Camci, E., Campolo, D., & Kayacan, E. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207490 · Cited by 17

Multi-Agent Active Search using Realistic Depth-Aware Noise Model

Ghods, R., Durkin, W. J., & Schneider, J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561598 · Cited by 17

A virtual simulation environment using deep learning for autonomous vehicles obstacle avoidance

Meftah, L. H., & Braham, R. (2020) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi49825.2020.9280513 · Cited by 17

Automatic estimation of optimal UAV flight parameters for real-time wide areas monitoring

Avola, D., Cinque, L., Fagioli, A., Foresti, G. L., Pannone, D., & Piciarelli, C. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10859-3 · Cited by 17

AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhicles

Esteves, T., Pinto, J. R., Ferreira, P. M., Costa, P. A., Rodrigues, L., Antunes, I., Lopes, G., Gamito, P. S., Abrantes, A. J., Jorge, P., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3128016 · Cited by 19

Unsettled Technology Areas in Autonomous Vehicle Test and Validation

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The State of Modeling, Simulation, and Data Utilization within Industry: An Autonomous Vehicles PerspectivePreprint

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