X-Driver: Explainable Autonomous Driving with Vision-Language Models

• URL: http://arxiv.org/abs/2505.05098v2
• Date: Tue, 03 Jun 2025 11:30:21 GMT
• Title: X-Driver: Explainable Autonomous Driving with Vision-Language Models
• Authors: Wei Liu, Jiyuan Zhang, Binxiong Zheng, Yufeng Hu, Yingzhan Lin, Zengfeng Zeng,
• Abstract summary: End-to-end autonomous driving has advanced significantly, offering benefits such as system simplicity and stronger driving performance.<n>Existing frameworks still suffer from low success rates in closed-loop evaluations, highlighting their limitations in real-world deployment.<n>We introduce X-Driver, a unified multi-modal large language models framework designed for closed-loop autonomous driving.
• Score: 6.053632514335829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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