Autonomous driving paper index
Applications of Large Language Models and Multimodal Large Models in Autonomous Driving: A Comprehensive Review
One-line summary
In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems.
Engineering notes
Key topics: autonomous driving system, autonomous driving, large language model, perception, prediction, planning. 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
The rapid development of large language models (LLMs) and multimodal large models (MLMs) has introduced transformative opportunities for autonomous driving systems. These advanced models provide robust support for the realization of more intelligent, safer, and efficient autonomous driving. In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems. First, we provide an overview of the evolution of LLMs and MLMs, along with a detailed analysis of the architecture of autonomous driving systems. Next, we explore the applications of LLMs and MLMs in key components such as perception, prediction, decision making, planning, multitask processing, and human–machine interaction. Additionally, this paper reviews the core technologies involved in integrating LLMs and MLMs with autonomous driving systems, including multimodal fusion, knowledge distillation, prompt engineering, and supervised fine tuning. Finally, we provide an in-depth analysis of the major challenges faced by autonomous driving systems powered by large models, offering new perspectives for future research. Compared to existing review articles, this paper not only systematically examines the specific applications of LLMs and MLMs in autonomous driving systems but also delves into the key technologies and potential challenges involved in their integration. By comprehensively organizing and analyzing the current literature, this review highlights the application potential of large models in autonomous driving and offers insights and recommendations for improving system safety and efficiency.
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