Title: EdgeDriver: optimising autonomous driving assistance with multi-LLM framework in cloud-edge computing environments

Authors: Yitian Zhu

Addresses: School of International, Beijing University of Posts and Telecommunications, Beijing, China

Abstract: Autonomous driving technology has recently made significant strides, laying the groundwork for the future of transportation systems. This advancement is increasingly powered by the emergence of Large Language Models (LLMs), which have become a focal point of research due to their remarkable common sense and reasoning ability. However, the deployment of LLMs is challenged by their computationally intensive nature, making direct implementation on mobile devices impractical. In this paper, we introduce EdgeDriver, an autonomous driving assistance framework leveraging multiple Large Language Models (LLMs) within cloud-edge computing environments to optimise computational efficiency and service quality. The framework comprises three modules: Perception, Planning and Evaluation, which process real-world driving scenarios to generate driving suggestions. Experiments were conducted using the BBD-100K data set, evaluating the impact of model parameters and input lengths on service quality. Results indicate that a 14B parameter model with 150-word input texts achieves optimal performance, balancing accuracy and computational efficiency. The study concludes that the quality of driving assistance services depends on matching input lengths and model capabilities to real-world conditions, providing valuable insights for future smart transportation systems.

Keywords: autonomous driving; large language models; mobile edge network; AIGC.

DOI: 10.1504/IJVICS.2025.147511

International Journal of Vehicle Information and Communication Systems, 2025 Vol.10 No.3, pp.285 - 298

Received: 19 Apr 2024
Accepted: 12 Jul 2024
Published online: 18 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article