Application and Future Prospects of Neural Network Testing in Autonomous Ships
Neural networks have widespread applications in safety-critical domains such as autonomous driving and medical diagnosis.However,the complexity and black-box nature of these systems present significant challenges in terms of reliability and safety.Neural network testing is a crucial means to address these challenges.This paper explores the importance of neural network testing,outlines the main testing methods,and analyzes the key challenges and future directions in this field.Additionally,this pa-per delves into the specific application scenario of autonomous ships,discussing the use of deep learning in autonomous ship naviga-tion and collision avoidance,particularly focusing on system intelligence and distributed intelligence strategies.This paper proposes a comprehensive approach by integrating neural network testing with autonomous ship technology to ensure the safety and reliability of autonomous ship systems.Through the application of adversarial testing,fuzz testing,coverage testing,and mutation testing,the robustness and adaptability of autonomous ships in complex marine environments are enhanced,laying a solid foundation for the fu-ture development of intelligent transportation systems.
neural network securitytestingautonomous shipsexplainable AI
万方数据
维普
