Advances of Safety Perception and Decision Making for Unmanned

Liu haichang, Liao shenghui

Abstract

The integration of large models into unmanned systems has marked a significant leap forward in the realm of safety perception and decision-making. This paper delves into the advancements achieved by harnessing the power of large-scale artificial intelligence models to enhance the capabilities of unmanned systems across various sectors. We explore the improved accuracy in environmental perception, the sophisticated decision-making processes, and the enhanced learning capabilities that result from the application of these models. The study evaluates the performance of unmanned systems in complex and dynamic environments, focusing on the contributions of deep learning and machine learning to safety and autonomy. Furthermore, the paper discusses the challenges and ethical considerations associated with the use of large models, including data privacy, model interpretability, and system robustness. The review concludes with an outlook on the future development of unmanned systems, emphasizing the potential for integration with emerging technologies and the pathways towards achieving higher levels of automation and safety.

Keywords

References

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