[3] [3] HUANG K L, SHI B T, LI X, et al. Multimodal sens fusion f auto driving perception: a survey[JOL]. ArXiv: 2202.02703.(20241216) [20241228] https:arxiv.ghtml2202.02703v3.

[4] G X WU, D B LI, H DING et al. An overview of developments and challenges for unmanned surface vehicle autonomous berthing. Complex & Intelligent Systems, 10, 981-1003(2024).

[5] C H ZHOU, S D GU, Y Q WEN et al. The review unmanned surface vehicle path planning: based on multi-modality constraint. Ocean Engineering, 200, 107043(2020).

[8] H KIM, D KIM, S M LEE. Marine object segmentation and tracking by learning marine radar images for autonomous surface vehicles. IEEE Sensors Journal, 23, 10062-10070(2023).

[10] N WANG, Y Y WANG, Y WEI et al. Marine vessel detection dataset and benchmark for unmanned surface vehicles. Applied Ocean Research, 142, 103835(2024).

[11] [11] CHEN X Z, MA H M, WAN J, et al. Multiview 3D object detection wk f autonomous driving[C]Proceedings of the IEEE conference on Computer Vision Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. doi: 10.1109CVPR.2017.691.

[12] K KIM, J KIM, J KIM. Robust data association for multi-object detection in maritime environments using camera and radar measurements. IEEE Robotics and Automation Letters, 6, 5865-5872(2021).

[14] J H PARK, M I ROH, H W LEE et al. Multi-vessel target tracking with camera fusion for unmanned surface vehicles. International Journal of Naval Architecture and Ocean Engineering, 16, 100608(2024).

[16] J Y LIN, P DIEKMANN, C E FRAMING et al. Maritime environment perception based on deep learning. IEEE Transactions on Intelligent Transportation Systems, 23, 15487-15497(2022).

[17] Q ZHANG, Y X SHAN, Z Q ZHANG et al. Multisensor fusion-based maritime ship object detection method for autonomous surface vehicles. Journal of Field Robotics, 41, 493-510(2024).

[18] [18] KIM J, SEONG M, BANG G, et al. RCMFusion: radarcamera multilevel fusion f 3D object detection[C]Proceedings of the 2024 IEEE International Conference on Robotics Automation (ICRA). Yokohama, Japan: IEEE, 2024. doi: 10.1109ICRA57147.2024.10611449.

[20] Y HUANG, Y M H XIAO, H D WANG et al. A rapid globe-wide shortest route planning algorithm based on two-layer oceanic shortcut network considering great circle distance. Ocean Engineering, 287, 115761(2023).

[22] R SONG, Y C LIU, R BUCKNALL. Smoothed A^* algorithm for practical unmanned surface vehicle path planning. Applied Ocean Research, 83, 9-20(2019).

[23] J WANG, R T WANG, D H LU et al. USV dynamic accurate obstacle avoidance based on improved velocity obstacle method. Electronics, 11, 2720(2022).

[24] [24] TAN Z K, WEI N X, LIU Z F. Local path planning f unmanned surface vehicle based on the improved dwa algithm[C]Proceedings of the 2022 41st Chinese Control Conference (CCC). Hefei, China: IEEE, 2022. doi: 10.23919CCC55666.2022.9901807.

[25] S Q MAO, P YANG, D J GAO et al. A motion planning method for unmanned surface vehicle based on improved RRT algorithm. Journal of Marine Science and Engineering, 11, 687(2023).

[26] J ZHAO, P R WANG, B Y LI et al. A DDPG-based USV path-planning algorithm. Applied Sciences, 13, 10567(2023).

[30] Z H PENG, C C MENG, L LIU et al. PWM-driven model predictive speed control for an unmanned surface vehicle with unknown propeller dynamics based on parameter identification and neural prediction. Neurocomputing, 432, 1-9(2021).

[31] LARRAZABAL J MENOYO, PEÑAS M SANTOS. Intelligent rudder control of an unmanned surface vessel. Expert Systems with Applications, 55, 106-117(2016).

[32] S B LI, T T MA, X Y LUO et al. Adaptive fuzzy output regulation for unmanned surface vehicles with prescribed performance. International Journal of Control, Automation and Systems, 18, 405-414(2020).

[33] Y Y ZHANG, S LI, J WENG. Learning and near-optimal control of underactuated surface vessels with periodic disturbances. IEEE Transactions on Cybernetics, 52, 7453-7463(2022).

[34] A B MARTINSEN, A M LEKKAS, S GROS. Reinforcement learning-based NMPC for tracking control of ASVs: theory and experiments. Control Engineering Practice, 120, 105024(2022).

[35] S S ØVERENG, D T NGUYEN, G HAMRE. Dynamic positioning using deep reinforcement learning. Ocean Engineering, 235, 109433(2021).

[36] N WANG, Y GAO, C YANG et al. Reinforcement learning-based finite-time tracking control of an unknown unmanned surface vehicle with input constraints. Neurocomputing, 484, 26-37(2022).

[37] T I FOSSEN. An adaptive line-of-sight (ALOS) guidance law for path following of aircraft and marine craft. IEEE Transactions on Control Systems Technology, 31, 2887-2894(2023).

[39] Y L LIAO, Z H JIA, W B ZHANG et al. Layered berthing method and experiment of unmanned surface vehicle based on multiple constraints analysis. Applied Ocean Research, 86, 47-60(2019).

[41] H T XU, P OLIVEIRA, SOARES C GUEDES. L1 adaptive backstepping control for path-following of underactuated marine surface ships. European Journal of Control, 58, 357-372(2021).

[44] Z K YAN, H D WANG, M Y ZHANG. Learning-based adaptive neural control for safer navigation of unmanned surface vehicle with variable mass. Ocean Engineering, 313, 119471(2024).

[46] [46] REDDING J, AMIN J, BOSKOVIC J, et al. Collabative mission planning, autonomy control technology (CoMPACT) f unmanned surface vehicles[C]AIAA Guidance, Navigation, & Control Conference. Chicago: AIAA, 2009. doi: 10.25146.20095774.

[47] Y C LIU, R SONG, R BUCKNALL et al. Intelligent multi-task allocation and planning for multiple unmanned surface vehicles (USVs) using self-organising maps and fast marching method. Information Sciences, 496, 180-197(2019).

[48] Y Q DUAN, J S EDWARDS, Y K DWIVEDI. Artificial intelligence for decision making in the era of big data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71(2019).

[49] [49] LIU J Q, HANG P, QI X, et al. MTDGPT: a multitask decisionmaking GPT model f autonomous driving at unsignalized intersections[C]2023 IEEE 26th International Conference on Intelligent Transptation Systems (ITSC). Bilbao, Spain: IEEE, 2023. doi: 10.1109ITSC57777.2023.10421993.