The Current Trends of Deep Learning in Autonomous Vehicles: A Review
by Raymond Ning Huang 1 
, Jing Ren2,* 
, Hossam A. Gabbar 3
1 Department of Mechanical Engineering, University of Toronto, Toronto, M5S 1A4, Canada
2 Department of Electrical and Computer Engineering, Ontario Tech University, Oshawa, L1H 7K4, Canada
3 Department of Energy and Nuclear Engineering, Ontario Tech University, Oshawa, L1H 7K4, Canada
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 10, Page # 56-68, 2022; DOI: 10.55708/js0110008
Keywords: Deep learning, Autonomous Vehicles, Control
Received: 15 August 2022, Revised: 07 October 2022, Accepted: 08 October 2022, Published Online: 31 October 2022
APA Style
Ren, J., Huang, R. N., Ren, J., & Gabbar, H. A. (2022). The Current Trends of Deep Learning in Autonomous Vehicles: A Review. Journal of Engineering Research and Sciences, 1(10), 56–68. https://doi.org/10.55708/js0110008
Chicago/Turabian Style
Ren, Jing, Raymond Ning Huang, Jing Ren, and Hossam A. Gabbar. “The Current Trends of Deep Learning in Autonomous Vehicles: A Review.” Journal of Engineering Research and Sciences 1, no. 10 (October 1, 2022): 56–68. https://doi.org/10.55708/js0110008.
IEEE Style
J. Ren, R. N. Huang, J. Ren, and H. A. Gabbar, “The Current Trends of Deep Learning in Autonomous Vehicles: A Review,” Journal of Engineering Research and Sciences, vol. 1, no. 10, pp. 56–68, Oct. 2022, doi: 10.55708/js0110008.
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Abstract
Autonomous vehicles are the future of road traffic. In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver. To realize this vision, seven essential technologies need to be evolved and refined including path planning, computer vision, sensor fusion, data security, fault diagnosis, control, and lastly, communication and networking. The contributions and the novelty of this paper are: 1) provide a comprehensive review of the recent advances in using deep learning for autonomous vehicle research, 2) offer insights into several important aspects of this emerging area, and 3) identify five directions for future research. To the best of our knowledge, there is no previous work that provides similar reviews for autonomous vehicle design.
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- J. Ren, H. Gaber and S. S. Al Jabar, “Applying Deep Learning to Autonomous Vehicles: A Survey,” 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), 2021, pp. 247-252, doi: 10.1109/ICAIBD51990.2021.9458968.
- C. Hodges, S. An, H. Rahmani, & M. Bennamoun. Deep Learning for Driverless Vehicles. In V. Balas, S. Roy, D. Sharma, & P. Samui (Eds.), Handbook of Deep Learning Applications pp. 83-99. ( Smart Innovation, Systems and Technologies; Vol. 136). Springer, 2019
- V. Rausch et al, “Learning a Deep Neural Net Policy for End-to-End Control of Autonomous Vehicles” American Control Conference, Seattle, May 2017.
- H. Jung et al, “Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning” IEEE Robotics and Automation Letters, 2018.
- K. Wu et al, “TDPP-Net: Achieving Three-Dimensional Path Planning via a Deep Neural Network Architecture” Neurocomputing, vol. 357, pp. 151-162, 2019.
- Z. Bai et al, “Deep Learning Based Motion Planning for Autonomous Vehicle Using Spatiotemporal LSTM Network” Chinese Automation Congress, 2018.
- K. Saleh, M. Hossny and S. Nahavandi, “Intent Prediction of Pedestrians via Motion Trajectories using Stacked Recurrent Neural Networks” IEEE Transaction on Intelligent Vehicles,vol. 3, no. 4, 2018.
- K. Makantasis et al, “Deep Reinforcement -Learning-Based Driving Policy for Autonomous Road Vehicles” IET Intelligent Transport Systems, 2020.
- K. Zhang et al, “Robot Navigation of Environments with Unknown Rough Terrain Using Deep Reinforcement Learning” Proc. IEE Int. Symp. Sat. Secur. Rescue Robot., 2018.
- C. Wang et al, “Autonomous Navigation of UAVs in Large-Scale Complex Environment: A Deep Reinforcement Learning Approach” IEEE Transactions on Vehicle Technology, 2019.
- H. Qie et al, “Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning,” IEEE Access, vol. 7, 2019.
- C. Wu et al, “UAV Autonomous Target Search Based on Deep Reinforcement Learning in Complex Disaster Scene,” IEEE Access, vol. 7, 2019.
- G-T. Tu and J. -G. Juang, “Path Planning and Obstacle Avoidance Based on Reinforcement Learning for UAV Application,” 2021 International Conference on System Science and Engineering (ICSSE), 2021, pp. 352-355, doi: 10.1109/ICSSE52999.2021.9537945.
- H. Zhai, W. Wang, W. Zhang and Q. Li, “Path Planning Algorithms for USVs via Deep Reinforcement Learning,” 2021 China Automation Congress (CAC), 2021, pp. 4281-4286, doi: 10.1109/CAC53003.2021.9728038.
- Liu, L. Shi, L. Sun, J. Li, M. Ding and F. Shu, “Path Planning for UAV-Mounted Mobile Edge Computing With Deep Reinforcement Learning,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5723-5728, May 2020, doi: 10.1109/TVT.2020.2982508.
- Wang, K. Wang, C. Pan, W. Xu, N. Aslam and L. Hanzo, “Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing,” in IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 1, pp. 73-84, March 2021, doi: 10.1109/TCCN.2020.3027695.
- Tang, J. Song, J. Ou, J. Luo, X. Zhang and K. -K. Wong, “Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method,” in IEEE Access, vol. 8, pp. 9124-9132, 2020, doi: 10.1109/ACCESS.2020.2964042.
- Chen, J. Jiang, N. Lv and S. Li, “An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge,” in IEEE Access, vol. 8, pp. 99059-99069, 2020, doi: 10.1109/ACCESS.2020.2998015.
- Arnold, O.Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis. “A Survey on 3D Object Detection Methods for Autonomous Driving Applications,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 10, Oct. 2019.
- Ravi Kumar et al., “SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10252-10261, Aug. 2022, doi: 10.1109/TITS.2021.3088950.
- Kosaka andG.Ohashi, “Vision-based night-time vehicle detection using CenSurE and SVM,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2599–2608, 2015.
- A. Yudina, A. Skrynnikb, A. Krishtopika, I. Belkina, and A. I. Panova, “Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection,” Optical Memory and Neural Networks (Information Optics), vol. 28, no. 4, p 283-295, 2019.
- Wang, and L. Zhou “Traffic Light Recognition With High Dynamic Range Imaging and Deep Learning,” IEEE Trans. Intell. Transp. Syst, vol. 20, no. 4, Apr. 2019.
- H. Kim, J. H. Park, and H. Y. Jung, “An Efficient Color Space for Deep-Learning Based Traffic Light Recognition,” Journal of Advanced Transportation, Dec. 2018.
- Z. Chen, and X. Huang, “Pedestrian Detection for Autonomous Vehicle Using Multi-Spectral Cameras,” IEEE Transactions on Intelligent Vehicles, vol. 4, No. 2, Jun. 2019.
- A. Amanatiadis, E. Karakasis, L. Bampis, S. Ploumpis, and A. Gasteratos, “ViPED: On-road vehicle passenger detection for autonomous vehicles,” Robotics and Autonomous Systems, Dec. 2018.
- Y. Li et al., “A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving,” in IEEE Access, vol. 8, pp. 194228-194239, 2020, doi: 10.1109/ACCESS.2020.3033289.
- X. Liu, and Z. Deng, “Segmentation of Drivable Road Using Deep Fully Convolutional Residual Network with Pyramid Pooling,” Cognitive Computation, Nov. 2017.
- D. K. Dewangan and S. P. Sahu, “Deep Learning-Based Speed Bump Detection Model for Intelligent Vehicle System Using Raspberry Pi,” in IEEE Sensors Journal, vol. 21, no. 3, pp. 3570-3578, 1 Feb.1, 2021, doi: 10.1109/JSEN.2020.3027097.
- A. Dhiman and R. Klette, “Pothole Detection Using Computer Vision and Learning,” in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 8, pp. 3536-3550, Aug. 2020, doi: 10.1109/TITS.2019.2931297.
- R. Xu, Y. Chen, X. Chen and S. Chen, “Deep learning based vehicle violation detection system,” 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), 2021, pp. 796-799, doi: 10.1109/ICSP51882.2021.9408935.
- Y. Wang, H. Tan, Y. Wu and J. Peng, “Hybrid Electric Vehicle Energy Management With Computer Vision and Deep Reinforcement Learning,” in IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 3857-3868, June 2021, doi: 10.1109/TII.2020.3015748.
- X. Tang, J. Chen, K. Yang, M. Toyoda, T. Liu and X. Hu, “Visual Detection and Deep Reinforcement Learning-Based Car Following and Energy Management for Hybrid Electric Vehicles,” in IEEE Transactions on Transportation Electrification, vol. 8, no. 2, pp. 2501-2515, June 2022, doi: 10.1109/TTE.2022.3141780.
- Y. Zhang et al., “Improved Short-Term Speed Prediction Using Spatiotemporal-Vision-Based Deep Neural Network for Intelligent Fuel Cell Vehicles,” in IEEE Transactions on Industrial Informatics, vol. 17, no. 9, pp. 6004-6013, Sept. 2021, doi: 10.1109/TII.2020.3033980.
- H. Unlu et al, “Sliding-Window Temporal Attention Based Deep Learning System for Robust Sensor Modality Fusion for UGV Navigation,” IEEE Robotics and Automation Letters, vol. 4, no. 4, 2019.
- F. Nobis et al, “A Deep Learning-Based Radar and Camera Sensor Fusion Architectur for Object Detection,” Sensor Data Fusion: Trends, Solutions, Applications,2019.
- X. Du et al, “Car Detection for Autonomous Vehicle: LIDAR and Vision Fusion Approach through Deep Learning Framework,” International Conference on Intelligent Robots and Systems, 2017.
- C. Luca et al, “Lidar-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks,” Robotics and Autonomous Systems, vol. 111, 2019.
- H. Liu and D. M. Blough, “MultiVTrain: Collaborative Multi-View Active Learning for Segmentation in Connected Vehicles,” 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2021, pp. 428-436, doi: 10.1109/MASS52906.2021.00060.
- A. Alireza et al, “Multimodal Vehicle Detection: Fusing 3D-LIDAR and Color Camera Data,” Pattern Recognition Letters, vol. 115, 2018.
- K. Geng et al, “Low-Observable Targets Detection for Autonomous Vehicles Based on Dual-Modal Sensor Fusion with Deep Learning Approach,” Proceedings of the Institutiion of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019.
- J. Nie, J. Yan, H. Yin, L. Ren and Q. Meng, “A Multimodality Fusion Deep Neural Network and Safety Test Strategy for Intelligent Vehicles,” in IEEE Transactions on Intelligent Vehicles, vol. 6, no. 2, pp. 310-322, June 2021, doi: 10.1109/TIV.2020.3027319.
- Z. Xiao et al, “Multimedia Fusion at Semantic Level in Vehicle Cooperative Perception,” IEEE International Conference on Multimedia & Expo Workshops, 2018.
- Wang, W. Hao and Y. Jin, “Fine-Grained Traffic Flow Prediction of Various Vehicle Types via Fusion of Multisource Data and Deep Learning Approaches,” in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 6921-6930, Nov. 2021, doi: 10.1109/TITS.2020.2997412.
- Li, M. Abdel-Aty, Q. Cai and Z. Islam, “A Deep Learning Approach to Detect Real-Time Vehicle Maneuvers Based on Smartphone Sensors,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3148-3157, April 2022, doi: 10.1109/TITS.2020.3032055.
- Chen, C. Jiang, S. Xiang, J. Ding, M. Wu and X. Li, “Smartphone Sensor-Based Human Activity Recognition Using Feature Fusion and Maximum Full a Posteriori,” in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 3992-4001, July 2020, doi: 10.1109/TIM.2019.2945467.
- Narayanan, A. Siravuru and B. Dariush, “Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data,” IEEE Robotics and Automation Letters, vol. 5, no. 2, 2020.
- Huang, C. Lv, Y. Xing and J. Wu, “Multi-Modal Sensor Fusion-Based Deep Neural Network for End-to-End Autonomous Driving With Scene Understanding,” in IEEE Sensors Journal, vol. 21, no. 10, pp. 11781-11790, 15 May15, 2021, doi: 10.1109/JSEN.2020.3003121.
- Xiong, Z. Cai, Q. Han, A. Alrawais and W. Li, “ADGAN: Protect Your Location Privacy in Camera Data of Auto-Driving Vehicles,” in IEEE Transactions on Industrial Informatics, vol. 17, no. 9, pp. 6200-6210, Sept. 2021, doi: 10.1109/TII.2020.3032352.
- Yang, Y. He and J. Qiao, “Federated Learning-Based Privacy-Preserving and Security: Survey,” 2021 Computing, Communications and IoT Applications (ComComAp), 2021, pp. 312-317, doi: 10.1109/ComComAp53641.2021.9653016.
- A. Ferrag, O. Friha, L. Maglaras, H. Janicke and L. Shu, “Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis,” in IEEE Access, vol. 9, pp. 138509-138542, 2021, doi: 10.1109/ACCESS.2021.3118642.
- Wang, Z. Su, N. Zhang and A. Benslimane, “Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing,” in IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1055-1069, 1 April-June 2021, doi: 10.1109/TNSE.2020.3014385.
- Kong et al., “Privacy-Preserving Aggregation for Federated Learning-Based Navigation in Vehicular Fog,” in IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 8453-8463, Dec. 2021, doi: 10.1109/TII.2021.3075683.
- Lu, Y. Yao and W. Shi, “CLONE: Collaborative Learning on the Edges,” in IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10222-10236, 1 July1, 2021, doi: 10.1109/JIOT.2020.3030278.
- Y. B. Lim et al., “Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach,” in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5140-5154, Aug. 2021, doi: 10.1109/TITS.2021.3056341
- Lu, X. Huang, K. Zhang, S. Maharjan and Y. Zhang, “Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4298-4311, April 2020, doi: 10.1109/TVT.2020.2973651.
- Kumar, R. Kumar, G. P. Gupta and R. Tripathi, “BDEdge: Blockchain and Deep-Learning for Secure Edge-Envisioned Green CAVs,” in IEEE Transactions on Green Communications and Networking, vol. 6, no. 3, pp. 1330-1339, Sept. 2022, doi: 10.1109/TGCN.2022.3165692.
- J. Q. Yu, “Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach,” in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4622-4634, July 2021, doi: 10.1109/TITS.2020.3036085.
- Jiang, H. Li, S. Liu, X. Luo and R. Lu, “Poisoning and Evasion Attacks Against Deep Learning Algorithms in Autonomous Vehicles,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4439-4449, April 2020, doi: 10.1109/TVT.2020.2977378.
- Song et al., “MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks,” 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2018, pp. 476-481, doi: 10.1109/ISVLSI.2018.00092.
- Zhao et al, “Deep Residual Networks with Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, 2018.
- He et al, “Improved Deep Transfer Auto-encoder for Fault Diagnosis of Gearbox under Variable Working Conditions with Small Training Samples,” IEEE Access, vol. 7, 2019.
- Kim et al, “A Deep Learning Part-Diagnosis Platform (DLPP) Based on an in-Vehicle on-Board Gateway for an Autonomous Vehicle,” KSII Transactions on Internet and Information Systems, vol. 13, no. 8, 2019.
- Jeong et al, “An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning,” Applied Science, 2018.
- Liu et al, “MRD-Nets: Multi-Scale Residual Networks w. Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal,” IEEE Access, vol. 7, 2019.
- Wolf et al, “Pre-ignition Detection Using Deep Neural Networks: A Step Towards Dat-Driven Automotive Diagnostics,” International Conferernce on Intelligent Transportation Systems, 2018.
- Olyael et al, “Fault Detection and Identification on UAV System with CITFA Algorithm Based on Deep Learning,” Iranian Conference on Electrical Engineering, 2018.
- -J. Lee, K. -T. Kim, J. -H. Park, G. Bere, J. J. Ochoa and T. Kim, “Convolutional Neural Network-Based False Battery Data Detection and Classification for Battery Energy Storage Systems,” in IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3108-3117, Dec. 2021, doi: 10.1109/TEC.2021.3061493.
- Moinul et al, “Deep Learning Based Micro-Grid Fault Detection and Classification in Future Smart Vehicle,” IEEE Transportation and Electrification Conference and Expo, 2018.
- Fu et al, “A Hybrid CNN-LSTM Model Based Actuator Fault Diagnosis for Six-Rotor UAVs,” Chinese Control and Decision Conference, 2019.
- Wang, X. Peng, M. Jiang and D. Liu, “Real-Time Fault Detection for UAV Based on Model Acceleration Engine,” in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9505-9516, Dec. 2020, doi: 10.1109/TIM.2020.3001659.
- Zou, W. Zhao, C. Wang and F. Chen, “Fault Detection Strategy of Vehicle Wheel Angle Signal via Long Short-Term Memory Network and Improved Sequential Probability Ratio Test,” in IEEE Sensors Journal, vol. 21, no. 15, pp. 17290-17299, 1 Aug.1, 2021, doi: 10.1109/JSEN.2021.3079118.
- van Wyk, Y. Wang, A. Khojandi and N. Masoud, “Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles,” in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1264-1276, March 2020, doi: 10.1109/TITS.2019.2906038.
- Raja, K. Kottursamy, K. Dev, R. Narayanan, A. Raja and K. B. V. Karthik, “Blockchain-Integrated Multiagent Deep Reinforcement Learning for Securing Cooperative Adaptive Cruise Control,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9630-9639, July 2022, doi: 10.1109/TITS.2022.3168486.
- G. Maciel-Pearson et al, “Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environment,” IEEE Robotics and Automation Letters, vol. 4, no. 4, 2019.
- K. Al-Sharman et al, “Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 69, issue 1, 2020.
- Kang et al, “Deep Convolutional Identifier for Dynamic Modeling and Adaptive Control of Unmanned Helicopter,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 2, 2019.
- Li et al, “Compensating Delays and Noises in Motion Control of Autonomous Electric Vehicles by Using Deep Learning and Unscented Kalman Predictor,” IEEE Transactions on Systems, Man and Cybernetics, 2018.
- Drews et al, “Vision-Based High-Speed Driving with a Deep Dynamic Observer,” IEEE Robotics and Automation Letters, vol. 4, no. 2, 2019.
- Zhang et al, “Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle,” IEEE Access, 2020.
- Carlucho et al, “Adaptive Low-Level Control of Autonomous Underwater Vehicles Using Deep Reinforcement Learning,” Robotics and Autonomous Systems, 2018.
- Shang and W. Qiao, “Intelligent driving trajectory tracking control algorithm based on deep learning,” 2021 IEEE 4th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 2021, pp. 618-621, doi: 10.1109/AUTEEE52864.2021.9668724.
- Sharma, G. Tewolde and J. Kwon, “Lateral and Longitudinal Motion Control of Autonomous Vehicles Using Deep Learning,” IEEE International Conference on Electro Information Technology, 2019.
- Yang et al, “Scene Understanding in Deep Learning-based End-to-End Controllers for Autonomous Vehicles,” IEEE Transaction on Systems, Man, and Cybernetics, vol. 49, no. 1, 2019.
- Zhu et al, “Hierarchical Decision and Control for Continuous Multitarget Problem: Policy Evaluation with Action Delay,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 2, 2019.
- Chen, W. Zhao, L. Li, C. Wang and F. Chen, “ES-DQN: A Learning Method for Vehicle Intelligent Speed Control Strategy Under Uncertain Cut-In Scenario,” in IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2472-2484, March 2022, doi: 10.1109/TVT.2022.3143840.
- Iwasaki and A. Okuyama, “Development of a Reference Signal Self-Organizing Control System Based on Deep Reinforcement Learning,” 2021 IEEE International Conference on Mechatronics (ICM), 2021, pp. 1-5, doi: 10.1109/ICM46511.2021.9385676.
- Chu, J. Wang, L. Codecà and Z. Li, “Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control,” in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1086-1095, March 2020, doi: 10.1109/TITS.2019.2901791.
- Boukerche, D. Zhong and P. Sun, “FECO: An Efficient Deep Reinforcement Learning-Based Fuel-Economic Traffic Signal Control Scheme,” in IEEE Transactions on Sustainable Computing, vol. 7, no. 1, pp. 144-156, 1 Jan.-March 2022, doi: 10.1109/TSUSC.2021.3138926.
- Yang, G. Wang, A. Sadeghi, G. B. Giannakis and J. Sun, “Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning,” in IEEE Transactions on Smart Grid, vol. 11, no. 3, pp. 2313-2323, May 2020, doi: 10.1109/TSG.2019.2951769.
- Duan et al., “Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations,” in IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 814-817, Jan. 2020, doi: 10.1109/TPWRS.2019.2941134.
- Kumari, K. K. Srinivas and P. Kumar, “Channel and Carrier Frequency Offset Equalization for OFDM Based UAV Communications Using Deep Learning,” in IEEE Communications Letters, vol. 25, no. 3, pp. 850-853, March 2021, doi: 10.1109/LCOMM.2020.3036493.
- H. Liu, Z. Chen, J. Tang, J. Xu and C. Piao, “Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach,” in IEEE Journal on Selected Areas in Communications, vol. 36, no. 9, pp. 2059-2070, Sept. 2018, doi: 10.1109/JSAC.2018.2864373.
- Wang, D. Deng, L. Xu and W. Wang, “Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks,” in IEEE Transactions on Communications, vol. 70, no. 6, pp. 3834-3848, June 2022, doi: 10.1109/TCOMM.2022.3170458.
- M Fadda, M. Murroni and V. Popescu, “Interference Issues for VANET Communications in the TVWS in Urban Environments,” in IEEE Transactions on Vehicular Technology, vol. 65, no. 7, pp. 4952-4958, July 2016, doi: 10.1109/TVT.2015.2453633.
- X Zhang, M. Peng, S. Yan and Y. Sun, “Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications,” in IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6380-6391, July 2020, doi: 10.1109/JIOT.2019.2962715.
- 0.S. Oubbati, M. Atiquzzaman, A. Baz, H. Alhakami and J. Ben-Othman, “Dispatch of UAVs for Urban Vehicular Networks: A Deep Reinforcement Learning Approach,” in IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13174-13189, Dec. 2021, doi: 10.1109/TVT.2021.3119070.
- U.Challita, W. Saad and C. Bettstetter, “Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach,” in IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2125-2140, April 2019, doi: 10.1109/TWC.2019.2900035.
- G. Cui, Y. Long, L. Xu and W. Wang, “Joint Offloading and Resource Allocation for Satellite Assisted Vehicle-to-Vehicle Communication,” in IEEE Systems Journal, vol. 15, no. 3, pp. 3958-3969, Sept. 2021, doi: 10.1109/JSYST.2020.3017710.
- T. Yang, J. Li, H. Feng, N. Cheng and W. Guan, “A Novel Transmission Scheduling Based on Deep Reinforcement Learning in Software-Defined Maritime Communication Networks,” in IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 1155-1166, Dec. 2019, doi: 10.1109/TCCN.2019.2939813.
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