面向自动驾驶的大模型对齐技术：综述

doi:10.19562/j.chinasae.qcgc.2024.11.001

Abstract

Abstract:

With the emergence of the Transformer attention mechanism， general-purpose large models represented by GPT have achieved the "emergence" of intelligence， bringing a dawn to the advancement towards higher levels of autonomous driving. Limited by the traditional from-scratch pre-training approach， which requires large-scale， high-quality， diverse autonomous driving data and incurs high training cost， the "large model + alignment technology" paradigm has been derived. As a bridge between general-purpose large models and autonomous driving， alignment technology， through customization methods such as fine-tuning or prompt engineering， achieves efficient and professional solutions to engineering problems within the field of autonomous driving. Alignment technology has become a hot research topic in the development of large models in vertical fields， but it lacks systematic research results. Based on this， this article firstly provides an overview of the development of autonomous driving and large model technology， thereby deriving alignment technology. Then， it reviews from the perspectives of fine-tuning and prompt engineering， systematically reviewing and analyzing the structure or performance characteristics of each classification technology， while providing actual application cases. Finally， based on existing research， the research challenges and development trends of alignment technology are proposed， offering references for promoting the advancement towards higher level of autonomous driving development.

Key words: autonomous driving, large model, review, alignment technology, fine-tuning, prompt engineering

Xiaolin Tang,Lu Gan,Guofa Li,Keqiang Li,Wenbo Chu. Large Model Alignment Technology for Autonomous Driving: A Review[J].Automotive Engineering, 2024, 46(11): 1937-1951.

Figures/Tables 10

References 119

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Large Model Alignment Technology for Autonomous Driving: A Review

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