End-to-end autonomous driving has advanced significantly, offering benefits such as system simplicity and stronger driving performance in both open-loop and closed-loop settings than conventional pipelines. However, existing frameworks still suffer from low success rates in closed-loop evaluations, highlighting their limitations in real-world deployment. In this paper, we introduce X-Driver, a unified multi-modal large language models(MLLMs) framework designed for closed-loop autonomous driving, leveraging Chain-of-Thought(CoT) and autoregressive modeling to enhance perception and decision-making. We validate X-Driver across multiple autonomous driving tasks using public benchmarks in CARLA simulation environment, including Bench2Drive[6]. Our experimental results demonstrate superior closed-loop performance, surpassing the current state-of-the-art(SOTA) while improving the interpretability of driving decisions. These findings underscore the importance of structured reasoning in end-to-end driving and establish X-Driver as a strong baseline for future research in closed-loop autonomous driving.

本研究针对现有自主驾驶系统在闭环评估中的低成功率问题，提出了X-Driver框架，该框架结合了链式思维和自回归建模，旨在提升感知与决策能力。实验结果显示，X-Driver在多个自主驾驶任务中优于现有最先进技术，显著提升了驾驶决策的可解释性，强调了结构化推理在终端驾驶中的重要性。

X-Driver：可解释的视觉语言模型自主驾驶