Artificial Intelligence-Based Techniques for Reducing Latency in Time-Critical Applications

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract: In today’s digital era, lowering system latency is critical for areas like autonomous driving, telemedicine, gaming, and financial trading. This survey reviews how AI helps minimize latency, especially within edge computing, fog systems, and 5G/6G networks. It outlines current optimization methods, their practical applications, and advanced solutions such as transfer learning and meta-learning to address cold start issues. The study also examines the fusion of AI with next-generation networks, where techniques like smart resource allocation, adaptive caching, and context-aware orchestration play a key role. Furthermore, it highlights major challenges, including energy efficiency, scalability of models, and system interoperability. This work provides a unified view of ongoing research and suggests directions for building intelligent, resilient, and low-latency digital infrastructures.

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