Large Models Efficient Compression Technology for Autonomous Driving:A Review
With the rapid development and widespread application of autonomous driving systems(ADS)globally,large models play a pivotal role in autonomous driving technology.These models integrate data from multiple sensors to achieve rapid and accurate understanding and decision-making in complex driving environments.However,large models face challenges such as massive parameter sizes,high computational costs,and large storage requirements,particularly accentuated in edge devices with limited resources.Efficiently compressing large models has become a significant research focus,enabling a reduction in computational and storage demands while maintaining performance.This study extensively explores the latest advancements and practical applications of large models technology,leading to the emergence of efficient compression techniques.It then analyzes various compression techniques,including pruning,neural network architecture search,low-rank decomposition,quantization,and knowledge distillation,in terms of their principles and performance characteristics.Finally,based on existing research,it outlines the future challenges and development directions of efficient compression techniques for large models,aiming to provide new insights and solutions for autonomous driving technology and drive the system towards higher efficiency,intelligence,and safety.
万方数据
维普
